Parameterization Schemes Research Articles

Modeling and Parameter Optimization of Plant Factory Production System Under Low-Carbon Constraints

Highlights A life cycle approach to modeling the carbon footprint of leafy vegetables. Determine the optimal design parameters through logical and mathematical relationship derivation. The simulation model of leaf vegetable production line was established. The optimal parameter scheme was determined by multifactorial simulation tests. Abstract. In recent years, a plant factory has gradually become a new model of facility agriculture, and the optimization problem of low-carbon design of production system in a plant factory has become more prominent. In the design of the production system, needs of high efficiency, high yield, and low carbon emission must be considered. In this study, the leafy vegetable factory is taken as the research object, and the carbon footprint calculation model of products is established based on the life cycle assessment method. Taking production capacity and carbon footprint as design indicators, the logical correlation and mathematical relationship between them and production line design parameters are analyzed layer by layer to determine the key design parameters. The leafy vegetable production line is modeled by plant simulation software, and multifactor simulation experiments are set up to explore the influence of key design parameters on design indexes to guide the optimal decision of parameter design scheme. The new design scheme increases the productivity of the plant factory by 35.40%, reduces the carbon footprint of leafy vegetables by 15.54%, and solves the problem that leafy vegetables at the bottom of the planting frame in the original plant factory grow poorly because of insufficient light. Keywords: Carbon footprint, Life cycle assessment, Plant factory, Product system, Production line modeling.

Hydrodynamics, Diagenesis and Hypoxia Variably Drive Benthic Oxygen Flux in a River‐Reservoir System

AbstractBenthic oxygen flux with complex spatiotemporal variations is essential for the global budget of carbon dioxide and the regional security of water quality and ecology, but its dominant driver under different circumstances has yet to be identified. In this study, a parametric scheme of oxygen flux was proposed and validated with aquatic eddy correlation measurements and then coupled with a diagenesis model and a water environment model. The coupled model was applied to a river‐reservoir with significant environmental gradients in hydrodynamics, diagenesis, and hypoxia, which are three factors that competitively drive the variation in benthic oxygen flux. The results indicate that hydrodynamics dominate the flux in the riverine and thalweg areas, diagenesis is the dominant driver of the lacustrine and bank areas, and hypoxia shows dominance only in the hypolimnetic anoxic area. In general, diagenesis is the dominant driver of oxygen flux in river‐reservoirs, followed by hydrodynamics, both of which are more prominent than hypoxia. If the operated reservoir experiences a wet year, the dominance of hydrodynamics tends to increase, while that of diagenesis and hypoxia decreases. The three divers exhibit similar but more stable dominance in riverine systems than in reservoirs, while diagenesis becomes the exclusive driver of oxygen fluxes in lacustrine systems.

Double Parametric Scheme Based Multi-objective Student Project Assignment Problem by Fuzzy Programming Technique with Linear Membership Function

Double Parametric Scheme Based Multi-objective Student Project Assignment Problem by Fuzzy Programming Technique with Linear Membership Function

Citywide Cellular Traffic Prediction Based on Densely Connected Convolutional Neural Networks

Abstract: This paper focuses on advancing citywide cellular traffic prediction for enhancing the self-management and intelligent automation of future cellular networks. The proposed approach employs deep learning to model the complex dynamics of wireless traffic. By representing traffic data as images, the method effectively captures both spatial and temporal dependencies in cell traffic using densely connected convolutional neural networks. To refine this model, a parametric matrix-based fusion scheme is introduced, enabling the learning of influence degrees associated with spatial and temporal dependencies. Experimental results demonstrate a substantial improvement in prediction performance, measured by root mean square error (RMSE), compared to existing algorithms. The accuracy of predictions is further validated using datasets from Telecom Italia

Integration of production–maintenance planning and monitoring simple linear profiles via Hotelling's T2 control chart and particle swarm optimization

This paper introduces an integrated model that merges three key concepts: quality control through linear profile monitoring using Hotelling's T2 control chart, production cost evaluation, and maintenance policies. The objective function of the integrated model is to minimize total costs. This study is the first to jointly consider production and maintenance costs in the framework of profile monitoring using Hotelling’s T2 control chart for linear profiles without compromising on the versatility of simple linear profiles as a modeling tool, particularly in calibration applications. Appropriate assumptions are made for the proposed integrated model and the particle swarm optimization algorithm is implemented to determine decision variables. To evaluate the effectiveness of the integrated model, a numerical example and a comprehensive comparative analysis are conducted. Moreover, a sensitivity analysis to study the effects of various input parameters on the optimal solutions is performed. The findings suggest that adopting the integrated model with specific decision variables results in lower expected total costs when compared to the joint model and fixed parameter schemes.

Analysis of fusion reactions induced by weakly bound nuclei on medium and heavy mass targets

Fusion cross section for reactions induced by weakly bound nuclei 6He, 6,7Li 9,11Be and 10B on targets lying in the mass region 64 ≤ A ≤ 238 have been investigated at around Coulomb barrier energies within the coupled channel formalism. Specifically, the effects of coupling to low-lying excited states of reactants and those arises due to the breakup of projectile have been studied by using Broglia and Winther (BW91), Aage Winther (AW95) and fitted potential (FP) parameterization schemes. Among these the FP scheme is found to be most appropriate in the description of fusion excitation functions of various projectile-target combination. Further, it is observed that in the sub barrier energy region there is an enhancement in the fusion cross section due to coupling to excited states while there occurs fusion suppression at above barrier energies because of the breakup of the projectile.

Validation of Two Newly Developed Albedo Schemes Based on the Observations Over the Region With Evergreen Broadleaved Forest in Southern China

AbstractExisting land surface models still have large error in the simulation of surface albedo. At present, introducing the influence of meteorological factors into the albedo parameterization scheme is an effective way to improve the albedo simulation. However, the improvement of the vegetation canopy surface albedo parameterization scheme is still insufficient. Therefore, based on the radiation and meteorological observation data during 1 November 2015–30 April 2018 from a land‐atmosphere interaction observation tower in a typical secondary evergreen broadleaved forest in Southern China, this study firstly analyzed the influencing factors of canopy surface albedo. It was found that the solar elevation angle and air relative humidity are two key factors influencing the canopy surface albedo, and the impact of the solar elevation angle on canopy surface albedo is not exactly the same on diurnal and seasonal scales. Then, two new canopy surface albedo parameterization schemes of additive form and multiplicative form were proposed and introduced into CLM5 model for single point simulation. Results show that the adoption of newly developed canopy surface albedo parameterization schemes can improve the simulation of diurnal and seasonal variations of albedo and reduce the overestimation of the simulation of near‐infrared radiation albedo and visible radiation albedo in the CLM5 model, then significantly reduce the root mean square errors of reflected solar radiation and net radiation simulation.

Design and parameter optimization of a new type in-wheel motor drive electric vehicle vibration reduction system

In order to reduce the problem of unsprung mass increased caused by using of in-wheel motor, resulting in poor ride comfort of in-wheel motor drive (IWMD) electric vehicle. A new type in-wheel motor drive electric vehicle vibration reduction system is designed based on the special structure of axial flux motor, and the stator of axial flux motor is suspended by rubber bushing and stator suspension. Then the effectiveness of the designed IWMD electric vehicle vibration reduction system is verified by simulation analysis. The parameters of the designed IWMD electric vehicle vibration reduction system is optimized by orthogonal experiment to further improve which vibration reduction performance, and the optimal parameters scheme of the designed IWMD electric vehicle vibration reduction system is determined by comparative simulation analysis.

Deep learning for quality control of surface physiographic fields using satellite Earth observations

Abstract. A purposely built deep learning algorithm for the Verification of Earth System ParametERization (VESPER) is used to assess recent upgrades to the global physiographic datasets underpinning the quality of the Integrated Forecasting System (IFS) of the European Centre for Medium-Range Weather Forecasts (ECMWF), which is used in both numerical weather prediction and climate reanalyses. A neural network regression model is trained to learn the mapping between the surface physiographic dataset, plus the main meteorologic fields from ERA5, and the MODIS satellite skin temperature observations. Once trained, this tool is applied to rapidly assess the quality of upgrades to the physiographic fields used by land surface schemes. Upgrades which improve the prediction accuracy of the machine learning tool indicate a reduction in the errors in the surface fields used as input to the surface parameterization schemes. Conversely, incorrect specifications of the surface fields decrease the accuracy with which VESPER can make predictions. We apply VESPER to assess the accuracy of recent upgrades to the permanent lake and glacier covers, as well as of planned upgrades to represent seasonally varying water bodies (i.e. ephemeral lakes). We show that, for grid cells where the lake fields have been updated, the prediction accuracy of VESPER in the land surface temperature (as quantified by the mean absolute error) improves by 0.37 K on average, whilst for the subset of points where the lakes have been completely removed and replaced with bare ground, the improvement is 0.83 K. We also show that updates to the glacier cover improve the prediction accuracy by 0.22 K. We highlight how neural networks such as VESPER can assist the research and development of surface parameterizations and their input physiography to better represent Earth’s surface coupled processes in weather and climate models.

Parameterizing pressure–temperature profiles of exoplanet atmospheres with neural networks

Context. Atmospheric retrievals (AR) of exoplanets typically rely on a combination of a Bayesian inference technique and a forward simulator to estimate atmospheric properties from an observed spectrum. A key component in simulating spectra is the pressure–temperature (PT) profile, which describes the thermal structure of the atmosphere. Current AR pipelines commonly use ad hoc fitting functions here that limit the retrieved PT profiles to simple approximations, but still use a relatively large number of parameters. Aims. In this work, we introduce a conceptually new, data-driven parameterization scheme for physically consistent PT profiles that does not require explicit assumptions about the functional form of the PT profiles and uses fewer parameters than existing methods. Methods. Our approach consists of a latent variable model (based on a neural network) that learns a distribution over functions (PT profiles). Each profile is represented by a low-dimensional vector that can be used to condition a decoder network that maps P to T. Results. When training and evaluating our method on two publicly available datasets of self-consistent PT profiles, we find that our method achieves, on average, better fit quality than existing baseline methods, despite using fewer parameters. In an AR based on existing literature, our model (using two parameters) produces a tighter, more accurate posterior for the PT profile than the five-parameter polynomial baseline, while also speeding up the retrieval by more than a factor of three. Conclusions. By providing parametric access to physically consistent PT profiles, and by reducing the number of parameters required to describe a PT profile (thereby reducing computational cost or freeing resources for additional parameters of interest), our method can help improve AR and thus our understanding of exoplanet atmospheres and their habitability.

Dynamically computed characteristic adjustment time scale for Zhang–McFarlane convective parameterization scheme

AbstractCharacteristic adjustment time scale τ is an important parameter in convective parameterization scheme. In this paper, we propose a dynamic calculation formula of τ in the Zhang–McFarlane scheme based on convective available potential energy (CAPE) in order to improve the simulation of precipitation. The performance of using the new formula is tested and evaluated using the Community Atmosphere Model (CAM5) for numerical experiments. The results indicate that the root mean square errors of mean precipitation amount over the Northern Hemisphere eastern equatorial Pacific Ocean, the central Indo-Pacific Warm Pool, the Tibetan Plateau, and northern South America are reduced by 20.15, 16.40, 12.56, and 6.45% in summer, respectively, after a dynamic τ is adopted. The dynamically computed τ improves the Pacific Walker circulation by strengthening the western ascending and eastern descending, and increases the intensity of deep convective precipitation in tropical areas by at least 1 mm/day. Dynamic τ also increases the frequency of large deep convective precipitation in the equatorial Pacific by almost 50%, and decreases the frequency of light deep convective precipitation in many other regions. The dynamic τ has positive effects in the areas with large CAPE and spatiotemporal variability, and also leads to an increase in deep convective precipitation and a significant decrease in large-scale precipitation over high-altitude regions of windward slopes. Therefore, the generation of exaggerated precipitation in the highlands is reduced in the CAM5. Overall, the dynamic calculation of characteristic adjustment time scale can improve precipitation simulation.

Improving the Forecasting of El Niño Amplitude Based on an Ensemble Forecast Strategy for Westerly Wind Bursts

Abstract The forecasting of El Niño amplitude remains uncertain, with false alarms or underestimations often occurring. It has been suggested that westerly wind bursts (WWBs) are crucial atmospheric stochastic forcing that affects the development, amplitude, and predictability of El Niño. Effectively capturing the influence of WWBs in El Niño forecasting systems to mitigate El Niño forecast uncertainties continues to be a widely discussed topic. In this work, two El Niño ensemble forecast scenarios incorporating WWBs were devised utilizing an intermediate coupled model capable of simulating the rational features of El Niño–Southern Oscillation and a refined WWB parameterization scheme. To start with, both the seasonal variations and the dependence on the oceanic background state were considered to improve the parameterization of WWBs. Furthermore, considering the short intrinsic predictability limit of WWBs with respect to the interannual scale of El Niño evolution, a WWB ensemble forecast strategy was designed to obtain their occurrence probability and the statistical features of their magnitude and central location. With this in mind, an ensemble forecast of El Niño events occurring during 1979–2021 based on a WWB ensemble forecast was established (termed WWB-based). For comparison, a conventional El Niño ensemble forecast based on initial condition (IC) perturbations was also conducted (termed IC-based). Results indicated that the WWB-based approach shows better performance in forecasting the El Niño amplitude than the IC-based one. The present approach suggests that an ensemble forecast with proper consideration of the scale interaction between the fast WWBs and interannual variations is more physically consistent. Significance Statement Westerly wind bursts (WWBs) are believed to influence the growth and amplitude of El Niño events. Unfortunately, the short intrinsic predictability limit of WWBs brings considerable uncertainty to the forecasting of El Niño amplitude. In this study, a refined parameterization scheme for WWBs, along with an ensemble forecast strategy designed to obtain the occurrence probability of WWBs and the statistical features of their magnitude and central location, was employed in an intermediate coupled model to produce an ensemble forecast for the El Niño events occurring during 1979–2021. The results showed that an ensemble forecast of El Niño based on WWB ensemble forecast can fully consider WWBs’ potential impact on the El Niño system and improve El Niño forecasts.

Benefit of incorporating GLASS remote sensing vegetation products in improving Noah-MP land surface temperature simulations on the Tibetan Plateau

Land Surface Temperature (LST) is important for diagnosing surface energy balance in land surface models (LSMs). However, LST simulation in current LSMs tends to show large cold biases, partially due to the reason that the model's prescribed vegetation parameters (e.g., Leaf Area Index (LAI) and Fraction of Vegetation Cover (FVC)) are misrepresented, especially in regions with complex topography and climate such as Tibetan Plateau. Recent advancements in remote sensing technologies provide a unique opportunity to improve the model's vegetation parameters at large scales. In this study, we practice two experiments to improve LST simulations in Noah-MP LSM by (1) incorporating LAI and FVC from the Global Land Surface Satellite (GLASS) remote sensing product (exp_RS); and (2) incorporating an empirical LAI and FVC parameterization scheme based on the soil temperature stress factor (exp_RL02). Results show that the effect of vegetation on simulated LST is the most significant in summer season when the model-satellite LAI and FVC differences are the largest. Compared to the default experiment that uses static LAI and FVC values from the model's look-up table (exp_CTL), the results in exp_RS and exp_RL02 show domain-wide improvement of the simulated LST. The LAI and FVC effect on LST are also well reflected in model's energy budget components (i.e., longwave emissivity, sensible and latent heat fluxes, etc). Validation of the model simulated soil temperature with in-situ observations further demonstrate the model improvements. Our study underscores the important role of vegetation in regulating surface energy transfer processes. Our study also highlights the feasibility and benefit of incorporating remote sensing data in improving land surface model simulations.

Sensitivity of summer precipitation simulation to the physical parameterizations in WRF over the Tibetan Plateau: A case study of 2018

The Tibetan Plateau (TP) holds a pivotal position in regional and global climate, whereas the atmospheric characteristics vary greatly in the simulation with different physical parameterization schemes at the gray-zone scale (around 9 km). In this study, 24 sets of experiments were set up with different cumulus parameterization schemes (CPSs), microphysics parameterization schemes (MPSs) and planetary boundary layer parameterization schemes (PBLSs) using the WRF model driven by the ERA5 reanalysis to explore the sensitivity and try to summarize a combination of parameterization schemes suitable for the simulation over TP. Based on comparisons against in-situ observations, most CPS experiments showed large wet bias and overestimated occurrence of precipitation events, and CTL without CPS and CU6 with Tiedtke CPS show better performance at gray-zone scale. The influence of MPS and PBLS is less than CPS on the simulation, and Thompson MPS and UW PBLS showed a better performance. Most of the CPS experiments reach an earlier and more intensive peak compared to the Integrated Multi-satellite Retrievals for GPM (IMERG) version 6 satellite precipitation product, except CTL, CU11, and CU6, which show closer but weaker peaks. CPS experiments simulate more precipitation associated with water vapor transporting into TP and anomalous cyclonic circulation against CTL. The less water vapor transport from the south edge of TP in the CTL against CU14, CU16, and CU2, leads to more local precipitation. Graupel particles and the warm-rain process play a crucial role in the precipitation simulation with different MPSs. Although with less water vapor in the low atmosphere, MP2 simulates more precipitation due to more graupel particles and warm-rain processes against CTL. PBLSs have a substantial impact on latent heat, then influence evaporation and precipitation. The lower PBL height usually leads to more latent heat and more precipitation.

Parameterized orographic gravity wave drag and dynamical effects in CMIP6 models

Orographic gravity waves (OGWs) are an important mechanism for coupling of the free atmosphere with the surface, mediating the momentum and energy transport and influencing the dynamics and circulation especially in the middle-atmosphere. Current global climate models are not able to resolve a large part of the OGW spectrum and hence, OGW effects have to be parameterized in the models. Typically, the only parameterized effect is the OGW induced drag. Despite producing the same quantity as an output and relying on similar assumptions (e.g. instantaneous vertical propagation), the individual OGW parameterization schemes differ in many aspects such as handling of the orography, the inclusion of non-linear effects near the surface and the tuning of the emergent free parameters. In this study, we have reviewed 7 different parameterizations, which are used in 9 different CMIP6 models. We report pronounced differences in the vertical distribution and magnitude of the parameterized OGW drag between the models and study to what extent the inter-model differences can be traced back to the difference in the type and tuning of the schemes. Finally, we demonstrate how the OGW drag differences project to the intermodel differences in the stratospheric dynamics. The study can pave the way for a more systematic research of the OGW parameterizations in the future, with an ultimate goal of lowering the amount of uncertainty of the future climate projections connected with the parameterized effects of unresolved orography.

Combining a New Parameterization Scheme of Oceanic Heat Flux with Thickness Assimilation to Improve Sea Ice Forecast Accuracy in Liaodong Bay

Liaodong Bay is one of the lowest latitude areas with seasonal sea ice cover in the Northern Hemisphere. Sea ice forecasting faces challenges in accuracy due to its low thickness. Therefore, a novel parameterization scheme for oceanic heat flux was developed to forecast sea ice thickness accurately. Application of the parameterization scheme for oceanic heat flux in the HIGHTSI model significantly improved the forecasting accuracy of sea ice thickness before the severe ice period. During this period, the RMSE of sea ice thickness measured on the JZ9–3 and the JZ20–2 oil platforms decreased by 0.53 cm and 2.90 cm compared to previous schemes, respectively. In order to improve the forecasting accuracy during the severe and melting ice periods, the observed and retrieved sea ice thickness was nudged into the model. The simulated results demonstrated a good agreement with monitored sea ice thickness distribution. During the entire season with sea ice cover, the R-squared values between simulated and retrieved sea ice thickness in the core area of Liaodong Bay reached 0.76. Furthermore, this study revealed a relatively strong correlation between air temperature and ice temperature on the following day. The proposed scheme provides a valuable approach to improve the forecasting accuracy for the areas with low thickness in the sea ice numerical models.

Dominant physical and chemical processes impacting nitrate in Shandong of the North China Plain during winter haze events

Nitrate has been a dominant component of PM2.5 since the stringent emission control measures implemented in China in 2013. Clarifying key physical and chemical processes influencing nitrate concentrations is crucial for eradicating heavy air pollution in China. In this study, we explored dominant processes impacting nitrate concentrations in Shandong of the North China Plain during three haze events from 9 to 25 December 2021, named cases P1 (94.46 (30.85) μg m−3 for PM2.5 (nitrate)), P2 (148.95 (50.12) μg m−3) and P3 (88.03 (29.21) μg m−3), by using the Weather Research and Forecasting/Chemistry model with an integrated process rate analysis scheme and updated heterogeneous hydrolysis of dinitrogen pentoxide on the wet aerosol surface (HET−N2O5) and additional nitrous acid (HONO) sources (AS−HONO). The results showed that nitrate increases in the three cases were attributed to aerosol chemistry, whereas nitrate decreases were due mainly to the vertical mixing process in cases P1 and P2 and to the advection process in case P3. HET−N2O5 (the reaction of OH + NO2) contributed 45 % (51 %) of the HNO3 production rate during the study period. AS−HONO produced a nitrate enhancement of 24 % in case P1, 12 % in case P2 and 19 % in case P3, and a HNO3 production rate enhancement of 0.79– 0.97 (0.18– 0.60) μg m−3 h−1 through the reaction of OH + NO2 (HET−N2O5) in the three cases. This study implies that using suitable parameterization schemes for heterogeneous reactions on aerosol and ground surfaces and nitrate photolysis is vital in simulations of HONO and nitrate, and the MOSAIC module for aerosol water simulations needs to be improved.

Observational energy transfers of a spiral cold filament within an anticyclonic eddy

The ocean surface mixed layer represents a critical interface linking the ocean and atmosphere. The physical processes determining the surface mixed layer properties and mediate atmosphere–ocean exchange. Submesoscale processes play a key role in cross-scale oceanic energy transformation and the determination of surface mixed-layer properties, includingthe enhancement of vertical nutrient transport, leading to increased primary productivity. Herein, we presented observations ofthe spiral chlorophyll-a filament and its influence on turbulence within an anticyclonic eddy in thewestern South China Sea during August 2021. The filament had a negative Ertel potential vorticity associated with strong upwelled/downward currents (approximately 20–40 m/day). Across-filament sections of the in-situ profiles showed turbulent dissipation rates enhanced inthe filament. We suggested this enhancement values can be attributed tosubmesoscale processes, which accounted for 25 % of the total parameterized turbulent dissipation rates. The present parametrized submesoscale turbulent scheme overestimated the in-situ values. The filament transferred kinetic energy upward to anticyclonic eddy via barotropic instability and gained energy from the anticyclonic eddy via baroclinic instability. After kinetic energy budget diagnostic, we suggested besides symmetric instability, centrifugal instability and mixed layer baroclinic instability should also be included in the turbulence scheme to overcome the overestimation. The observeddual energy transfers between the anticyclonic eddy and filament, and the observed high turbulent energy dissipation within the filament, emphasized the need for these processes to be accurately parameterized regional and climate models.

Bathymetry-Aware Mesoscale Eddy Parameterizations across Upwelling Slope Fronts: A Machine Learning–Augmented Approach

Abstract Mesoscale eddy buoyancy fluxes across continental slopes profoundly modulate the boundary current dynamics and shelf–ocean exchanges but have yet to be appropriately parameterized via the Gent–McWilliams (GM) scheme in predictive ocean models. In this work, we test the prognostic performance of multiple GM variants in noneddying simulations of upwelling slope fronts that are commonly found along the subtropical continental margins. The tested GM variants range from a set of constant eddy buoyancy diffusivities to recently developed energetically constrained, bathymetry-aware diffusivities, whose implementation is augmented by an artificial neural network (ANN) serving to predict the mesoscale eddy energy based on the topographic and mean flow quantities online. In addition, an ANN is employed to parameterize the cross-slope eddy momentum flux (EMF) that maintains a barotropic flow field analogous to that in an eddy-resolving model. Our tests reveal that noneddying simulations employing the bathymetry-aware forms of the Rhines scale–based scheme and GEOMETRIC scheme can most accurately reproduce the heat contents and along-slope baroclinic transports as those in the eddy-resolving simulations. Further analyses reveal certain degrees of physical consistency in the ANN-inferred eddy energy, which tends to grow (decay) as isopycnal slopes are steepened (flattened), and in the parameterized EMF, which exhibits the correct strength of shaping the flow baroclinicity if a bathymetry-aware GM variant is jointly used. These findings provide a recipe of GM variants for use in noneddying simulations with continental slopes and highlight the potential of machine learning techniques to augment physics-based mesoscale eddy parameterization schemes. Significance Statement This study evaluates the predictive skill of parameterization schemes of water mass transports induced by ocean mesoscale eddies across continental slopes. Correctly parameterizing these transports in noneddying ocean models (e.g., ocean climate models) is crucial for predicting the ocean circulation and shelf–ocean exchanges. This work highlights the importance of bathymetric effects on eddy transports, as parameterization schemes that account for the influence of a sloping seafloor outperform those developed specifically for a flat-bottomed ocean. This work also highlights the efficacy of machine learning techniques to augment physics-based mesoscale eddy parameterization schemes, for instance, by estimating the mesoscale eddy energy online to realize energy-dependent parameterization schemes in noneddying simulations.

Modeling land-atmosphere energy and water exchanges in the typical alpine grassland in Tibetan Plateau using Noah-MP

Study regionAlpine grassland in the Tibetan Plateau Study focusThis study used Noah-MP to simulate the energy fluxes [net radiation (Rn), latent (λE) and sensible (H) heat fluxes] in two typical alpine ecosystems in the Tibetan Plateau. The effectivenesses of new parameterization schemes for the roughness length for heat transfer and the root distribution were evaluated. By further using multi-scenario modeling, the responses of land evapotranspiration (ET), soil evaporation (Es) and vegetation transpiration (Tr) to changes in precipitation, solar radiation and air temperature were investigated. New hydrological insights for the regionThe default Noah-MP largely overestimates (underestimates) Rn and λE, but tends to underestimate (overestimate) H in alpine meadow (alpine steppe). After incorporating two newly-developed parameterization schemes, the accurancy of Noah-MP has been improved in alpine meadow for Rn and λE, while in alpine steppe only H has been improved. The ET, Es and Tr in alpine meadow are more sensitive to decreasing precipitation than that to increasing precipitation. In alpine steppe, ET and Es increase with increasing precipitation, while Tr responds weakly to changes in precipitation; The effect of temperature change on ET appears weak in both ecosystems. However, Tr shows negative responses to increased temperature and such responses in alpine steppe are more remarkable than those in alpine meadow, suggesting the former may be more vulnerable to future warming than the latter.