Implication of community-level ecophysiological parameterization to modelling ecosystem productivity:a case study across nine contrasting forest sites in eastern China

Parameterization is a critical step in modelling ecosystem dynamics.However,assigning parameter values can be a technical challenge for structurally complex natural plant communities;uncertainties in model simulations often arise from inappropriate model parameterization.Here we compared five methods for defining community-level specific leaf area(SLA)and leaf C:N across nine contrasting forest sites along the North-South Transect of Eastern China,including biomass-weighted average for the entire plant community(AP_BW)and four simplified selective sampling(biomass-weighted average over five dominant tree species[5DT_BW],basal area weighted average over five dominant tree species[5DT_AW],biomass-weighted average over all tree species[AT_BW]and basal area weighted average over all tree species[AT_AW]).We found that the default values for SLA and leaf C:N embedded in the Biome-BGC v4.2 were higher than the five computational methods produced across the nine sites,with deviations ranging from 28.0 to 73.3%.In addition,there were only slight deviations(<10%)between the whole plant community sampling(AP_BW)predicted NPP and the four simplified selective sampling methods,and no significant difference between the predictions of AT_BW and AP_BW except the Shennongjia site.The findings in this study highlights the critical importance of computational strategies for community-level parameterization in ecosystem process modelling,and will support the choice of parameterization methods.

Parameterization, sensitivity, and uncertainty of 1-D thermodynamic thin-ice thickness retrieval

Retrieval of Thin-Ice Thickness(TIT)using thermodynamic modeling is sensitive to the parameterization of the independent variables(coded in the model)and the uncertainty of the measured input variables.This article examines the deviation of the classical model’s TIT output when using different parameterization schemes and the sensitivity of the output to the ice thickness.Moreover,it estimates the uncertainty of the output in response to the uncertainties of the input variables.The parameterized independent variables include atmospheric longwave emissivity,air density,specific heat of air,latent heat of ice,conductivity of ice,snow depth,and snow conductivity.Measured input parameters include air temperature,ice surface temperature,and wind speed.Among the independent variables,the results show that the highest deviation is caused by adjusting the parameterization of snow conductivity and depth,followed ice conductivity.The sensitivity of the output TIT to ice thickness is highest when using parameterization of ice conductivity,atmospheric emissivity,and snow conductivity and depth.The retrieved TIT obtained using each parameterization scheme is validated using in situ measurements and satellite-retrieved data.From in situ measurements,the uncertainties of the measured air temperature and surface temperature are found to be high.The resulting uncertainties of TIT are evaluated using perturbations of the input data selected based on the probability distribution of the measurement error.The results show that the overall uncertainty of TIT to air temperature,surface temperature,and wind speed uncertainty is around 0.09 m,0.049 m,and−0.005 m,respectively.

A New Result on Regular Designs under Baseline Parameterization

The study on designs for the baseline parameterization has aroused attention in recent years. This paper focuses on two-level regular designs for the baseline parameterization. A general result on the relationship between K-aberration and word length pattern is developed.

Performance of physical-informed neural network (PINN) for the key parameter inference in Langmuir turbulence parameterization scheme

The Stokes production coefficient(E_(6))constitutes a critical parameter within the Mellor-Yamada type(MY-type)Langmuir turbulence(LT)parameterization schemes,significantly affecting the simulation of turbulent kinetic energy,turbulent length scale,and vertical diffusivity coefficient for turbulent kinetic energy in the upper ocean.However,the accurate determination of its value remains a pressing scientific challenge.This study adopted an innovative approach by leveraging deep learning technology to address this challenge of inferring the E_(6).Through the integration of the information of the turbulent length scale equation into a physical-informed neural network(PINN),we achieved an accurate and physically meaningful inference of E_(6).Multiple cases were examined to assess the feasibility of PINN in this task,revealing that under optimal settings,the average mean squared error of the E_(6) inference was only 0.01,attesting to the effectiveness of PINN.The optimal hyperparameter combination was identified using the Tanh activation function,along with a spatiotemporal sampling interval of 1 s and 0.1 m.This resulted in a substantial reduction in the average bias of the E_(6) inference,ranging from O(10^(1))to O(10^(2))times compared with other combinations.This study underscores the potential application of PINN in intricate marine environments,offering a novel and efficient method for optimizing MY-type LT parameterization schemes.

Application of the two different salinity parameterization schemes in the sea ice model

In this study, we mainly introduce two salinity parameterization schemes used in Sea Ice Simulator （SIS）, that is, isosaline scheme and salinity profile scheme. Comparing the equation of isosaline scheme with that of salinity profile scheme, we found that there was one different term between the two schemes named the salinity different term. The thermodynamic effect of the salinity difference term on sea ice thickness and sea ice concentration showed that： in the freezing processes from November to next May, the sea ice temperature could rise on the influence of the salinity difference term and restrain sea ice freezing; at the first melting phase from June to August, the upper ice melting rate was faster than the lower ice melting rate. Then sea ice temperature could rise and accelerate the sea ice melting; at the second melting phase from September to October, the upper ice melting rate was slower than the lower ice melting rate, then sea ice temperature could decrease and restrain sea ice melting. However, the effect of the salinity difference term on the sea ice thickness and sea ice concentration was weak. To analyze the impacts of the salinity different term on Arctic sea ice thickness and sea ice concentration, we also designed several experiments by introducing the two salinity parameterizations to the ice-ocean coupled model, Modular Ocean Model （MOM4）, respectively. The simulated results confirmed the previous results of formula derivation.

Optimal parameterization of conic curves

Optimal parameterization of specified segment on the algebraic curves is a hot issue in CAGD and CG. Take the optimal approximation of arc-length parameterization as the criterion of optimal parameterization, and the optimal or close to optimal rational parameterization formula of any specified segment on the conic curves is obtained. The new method proposed in this paper has ad- vantage in quantity of calculation and has strong self-adaptability. Finally, a experimental comparison of the results obtained by this method and by the traditional parametric algorithm is conducted.

Optimal Rational Parameterization of Quadratic Curves Based on the Geographic Information of Both Ends

In this paper, we rewrote the equation of algebraic curve segmentswith the geometric informationonboth ends. The optimal or nearly optimal rationalparametric equation is determinedbythe principle that parametricspeedsat both endsareequal. Comparing withotherliteratures, the methodofthis paper has advantage in efficiency andiseasy to realize. The equation of optimal rational parameterization can be obtained directly by the information of both ends. Large numbers ofexperimental data show that our method hasbeen given withmore self-adaptability and accuracy than that ofotherliteratures, and if the parametricspeedat any end reaches its maximum or minimum value, the parameterization is optimal; otherwise itis close tooptimal rational parameterization.

Improving the CoLM in Taklimakan Desert Hinterland with Accurate Key Parameters and an Appropriate Parameterization Scheme

Improving and validating land surface models based on integrated observations in deserts is one of the challenges in land modeling. Particularly, key parameters and parameterization schemes in desert regions need to be evaluated in-situ to improve the models. In this study, we calibrated the land-surface key parameters and evaluated several formulations or schemes for thermal roughness length （z 0h ） in the common land model （CoLM）. Our parameter calibration and scheme evaluation were based on the observed data during a torrid summer （29 July to 11 September 2009） over the Taklimakan Desert hinterland. First, the importance of the key parameters in the experiment was evaluated based on their physics principles and the significance of these key parameters were further validated using sensitivity test. Second, difference schemes （or physics-based formulas） of z 0h were adopted to simulate the variations of energy-related variables （e.g., sensible heat flux and surface skin temperature） and the simulated variations were then compared with the observed data. Third, the z 0h scheme that performed best （i.e., Y07） was then selected to replace the defaulted one （i.e., Z98）; the revised scheme and the superiority of Y07 over Z98 was further demonstrated by comparing the simulated results with the observed data. Admittedly, the revised model did a relatively poor job of simulating the diurnal variations of surface soil heat flux, and nighttime soil temperature was also underestimated, calling for further improvement of the model for desert regions.

The Influences of Boundary Layer Parameterization Schemes on Mesoscale Heavy Rain System

The mesoscale numerical weather prediction model (MM4) in which the computations of the turbulent exchange coefficient in the boundary layer and surface fluxes are improved, is used to study the influences of boundary layer parameterization schemes on the predictive results of the mesoscale model. Seven different experiment schemes (including the original MM4 model) designed in this paper are tested by the observational data of several heavy rain cases so as to find an improved boundary layer parameterization scheme in the mesoscale meteorological model. The results show that all the seven different boundary layer parameterization schemes have some influences on the forecasts of precipitation intensity, distribution of rain area, vertical velocity, vorticity and divergence fields, and the improved schemes in this paper can improve the precipitation forecast. Key words Boundary layer parameterization - Mesoscale numerical weather prediction (MNWP) - Turbulent exchange coefficient - Surface fluxes - Heavy rain This paper was supported by the National Natural Science Foundation of China (Grant No. 49875005 and No. 49735180).

A Parameterization Scheme of Surface Turbulent Momentum and Sensible Heat over the Gobi Underlying Surface

The parameterization of surface turbulent fluxes over the Gobi Desert in arid regions is studied by using rationally screened observational data. First, the characteristics of Monin-Obukhov similarity functions are analyzed and their empirical formulae are fitted. The results show that fitted curves of changes of similarity functions of wind speed and temperature with stability parameter differ little from the typical empirical curves and are within the ranges of scatter of the empirical curves, but their values in the neutral condition arc different from the typical values to some extent. Furthermore, average values of momentum and scalar (sensible heat) roughness lengths as well as changes of scalar roughness length with friction velocity are determined by utilizing the data. It is found that the average values of scalar roughness length are about one order smaller than that of the momentum roughness length and decrease with increasing friction velocity, but they are evidently larger than their theoretically forecasted values.

On the Radiative Properties of Ice Clouds:Light Scattering, Remote Sensing,and Radiation Parameterization

Presented is a review of the radiative properties of ice clouds from three perspectives： light scattering simulations, remote sensing applications, and broadband radiation parameterizations appropriate for numerical models. On the subject of light scattering simulations, several classical computational approaches are reviewed, including the conventional geometric-optics method and its improved forms, the finite-difference time domain technique, the pseudo-spectral time domain technique, the discrete dipole approximation method, and the T-matrix method, with specific applications to the computation of the singlescattering properties of individual ice crystals. The strengths and weaknesses associated with each approach are discussed.With reference to remote sensing, operational retrieval algorithms are reviewed for retrieving cloud optical depth and effective particle size based on solar or thermal infrared（IR） bands. To illustrate the performance of the current solar- and IR-based retrievals, two case studies are presented based on spaceborne observations. The need for a more realistic ice cloud optical model to obtain spectrally consistent retrievals is demonstrated. Furthermore, to complement ice cloud property studies based on passive radiometric measurements, the advantage of incorporating lidar and/or polarimetric measurements is discussed.The performance of ice cloud models based on the use of different ice habits to represent ice particles is illustrated by comparing model results with satellite observations. A summary is provided of a number of parameterization schemes for ice cloud radiative properties that were developed for application to broadband radiative transfer submodels within general circulation models（GCMs）. The availability of the single-scattering properties of complex ice habits has led to more accurate radiation parameterizations. In conclusion, the importance of using nonspherical ice particle models in GCM simulations for climate studies is proven.

Effects of Surface Flux Parameterization on the Numerically Simulated Intensity and Structure of Typhoon Morakot(2009)

The effects of surface flux parameterizations on tropical cyclone（TC） intensity and structure are investigated using the Advanced Research Weather Research and Forecasting（WRF-ARW） modeling system with high-resolution simulations of Typhoon Morakot（2009）.Numerical experiments are designed to simulate Typhoon Morakot（2009） with different formulations of surface exchange coefficients for enthalpy（C_K） and momentum（C_D） transfers,including those from recent observational studies based on in situ aircraft data collected in Atlantic hurricanes.The results show that the simulated intensity and structure are sensitive to C_K and C_D,but the simulated track is not.Consistent with previous studies,the simulated storm intensity is found to be more sensitive to the ratio of C_K/C_D than to C_K or C_D alone.The pressure-wind relationship is also found to be influenced by the exchange coefficients,consistent with recent numerical studies.This paper emphasizes the importance of C_D and C_K on TC structure simulations.The results suggest that C_D and C_K have a large impact on surface wind and flux distributions,boundary layer heights,the warm core,and precipitation.Compared to available observations,the experiment with observed C_D and C_K generally simulated better intensity and structure than the other experiments,especially over the ocean.The reasons for the structural differences among the experiments with different C_D and C_K setups are discussed in the context of TC dynamics and thermodynamics.

Evaluating and Improving Wind Forecasts over South China: The Role of Orographic Parameterization in the GRAPES Model

Unresolved small-scale orographic（SSO） drags are parameterized in a regional model based on the Global/Regional Assimilation and Prediction System for the Tropical Mesoscale Model（GRAPES TMM）. The SSO drags are represented by adding a sink term in the momentum equations. The maximum height of the mountain within the grid box is adopted in the SSO parameterization（SSOP） scheme as compensation for the drag. The effects of the unresolved topography are parameterized as the feedbacks to the momentum tendencies on the first model level in planetary boundary layer（PBL）parameterization. The SSOP scheme has been implemented and coupled with the PBL parameterization scheme within the model physics package. A monthly simulation is designed to examine the performance of the SSOP scheme over the complex terrain areas located in the southwest of Guangdong. The verification results show that the surface wind speed bias has been much alleviated by adopting the SSOP scheme, in addition to reduction of the wind bias in the lower troposphere. The target verification over Xinyi shows that the simulations with the SSOP scheme provide improved wind estimation over the complex regions in the southwest of Guangdong.

A Study on Parameterization of Surface Albedo over Grassland Surface in the Northern Tibetan Plateau

The relationship of surface albedo with the solar altitude angle and soil moisture is analyzed based on two-year （January 2002 to December 2003） observational data from the AWS （Automatic Weather Station） at MS3478 in the northern Tibetan Plateau during the experimental period of CEOP/CAMP-Tibet （Coordinated Enhanced Observing Period Asia-Australia Monsoon Project on the Tibetan Plateau）. As a double-variable （solar altitude angle and soil moisture） function, surface albedo varies inconspicuously with any single factor. By using the method of approximately separating the double-variable function into two, one-factor functions （product and addition）, the relationship of albedo with these two factors presents much better. The product and additional empirical formulae of albedo are then preliminarily fitted based on long-term experimental data. By comparison with observed values, it is found that the parameterization formulae fitted by using observational data are mostly reliable and their correlation coefficients are both over 0.6. The empirical formulae of albedo though, for the northern Tibetan Plateau, need to be tested by much more representative observational data with the help of numerical models and the retrieval of remote sensing data. It is practical until it is changed into effective parameterization formulae representing a grid scale in models.

Parameterization of Heat Fluxes at Heterogeneous Surfaces by Integrating Satellite Measurements with Surface Layer and Atmospheric Boundary Layer Observations

The regional heat flux exchange between heterogeneous landscapes and the nearby surface layer （SL） is a key issue in the study of land-atmosphere interactions over arid areas such as the Heihe River basin in northwestern China and in high elevation areas such as the Tibetan Plateau. Based on analysis of the land surface heterogeneity and its effects on the overlying air flow, the use of SL observations, atmospheric boundary layer （ABL） observations, and satellite remote sensing （RS） measurements along with three parameterization methodologies （here, termed as the RS, tile, and blending approaches） have been proposed to estimate the surface heat flux densities over heterogeneous landscapes. The tile and blending approaches have also been implemented during HEIhe basin Field Experiment （HEIFE）, the Coordinated Enhanced Observing Period （CEOP） Asia-Australia Monsoon Project on the Tibetan Plateau （CAMP/Tibet）, the Arid Environment Comprehensive Monitoring Plan ＇95 （AECMP＇95）, and the DunHuang Experiment （DHEX）. The results showed that these two proposed parameterization methodologies can be accurately used over heterogeneous land surfaces.

Performance of the Wind Farm Parameterization Scheme Coupled with the Weather Research and Forecasting Model under Multiple Resolution Regimes for Simulating an Onshore Wind Farm

We use the Wind Farm Parameterization(WFP) scheme coupled with the Weather Research and Forecasting model under multiple resolution regimes to simulate turbulent wake dynamics generated by a real onshore wind farm and their influence at the local meteorological scale. The model outputs are compared with earlier modeling and observation studies. It is found that higher vertical and horizontal resolutions have great impacts on the simulated wake flow dynamics. The corresponding wind speed deficit and turbulent kinetic energy results match well with previous studies. In addition, the effect of horizontal resolution on near-surface meteorology is significantly higher than that of vertical resolution. The wake flow field extends from the start of the wind farm to downstream within 10 km, where the wind speed deficit may exceed 4%. For a height of 150 m or at a distance of about 25 km downstream, the wind speed deficit is around 2%. This indicates that, at a distance of more than 25 km downstream, the impact of the wind turbines can be ignored. Analysis of near-surface meteorology indicates a night and early morning warming near the surface, and increase in near-surface water vapor mixing ratio with decreasing surface sensible and latent heat fluxes. During daytime, a slight cooling near the surface and decrease in the near-surface water vapor mixing ratio with increasing surface sensible and latent heat fluxes is noticed over the wind farm area.

Numerical Study of Boundary Layer Structure and Rainfall after Landfall of Typhoon Fitow(2013): Sensitivity to Planetary Boundary Layer Parameterization

The boundary layer structure and related heavy rainfall of Typhoon Fitow(2013), which made landfall in Zhejiang Province, China, are studied using the Advanced Research version of the Weather Research and Forecasting model, with a focus on the sensitivity of the simulation to the planetary boundary layer parameterization. Two groups of experiments—one with the same surface layer scheme and including the Yonsei University(YSU), Mellor–Yamada–Nakanishi–Niino Level 2.5,and Bougeault and Lacarrere schemes; and the other with different surface layer schemes and including the Mellor–Yamada–Janjic′ and Quasi-Normal Scale Elimination schemes—are investigated. For the convenience of comparative analysis, the simulation with the YSU scheme is chosen as the control run because this scheme successfully reproduces the track, intensity and rainfall as a whole. The maximum deviations in the peak tangential and peak radial winds may account for 11% and 33%of those produced in the control run, respectively. Further diagnosis indicates that the vertical diffusivity is much larger in the first group, resulting in weaker vertical shear of the tangential and radial winds in the boundary layer and a deeper inflow layer therein. The precipitation discrepancies are related to the simulated track deflection and the differences in the simulated low-level convergent flow among all tests. Furthermore, the first group more efficiently transfers moisture and energy and produces a stronger ascending motion than the second, contributing to a deeper moist layer, stronger convection and greater precipitation.

THE IMPACT OF DIFFERENT PHYSICAL PROCESSES AND THEIR PARAMETERIZATIONS ON FORECAST OF A HEAVY RAINFALL IN SOUTH CHINA IN ANNUALLY FIRST RAINING SEASON

An ensemble prediction system based on the GRAPES model, using multi-physics, is used to discuss the influence of different physical processes in numerical models on forecast of heavy rainfall in South China in the annually first raining season(AFRS). Pattern, magnitude and area of precipitation, evolution of synoptic situation, as well as apparent heat source and apparent moisture sink between different ensemble members are comparatively analyzed. The choice of parameterization scheme for land-surface processes gives rise to the largest influence on the precipitation prediction. The influences of cumulus-convection and cloud-microphysics processes are mainly focused on heavy rainfall;the use of cumulus-convection parameterization tends to produce large-area and light rainfall. Change in parameterization schemes for land-surface and cumulus-convection processes both will cause prominent change in forecast of both dynamic and thermodynamic variables, while change in cloud-microphysics processes show primary impact on dynamic variables. Comparing simplified Arakawa-Schubert and Kain-Fritsch with Betts-Miller-Janjic schemes, SLAB with NOAH schemes, as well as both WRF single moment 6-class and NCEP 3-class with simplified explicit schemes of phase-mixed cloud and precipitation shows that the former predicts stronger low-level jets and high humidity concentration, more convective rainfall and local heavy rainfall, and have better performance in precipitation forecast. Appropriate parameterization schemes can reasonably describe the physical process related to heavy rainfall in South China in the AFRS, such as low-level convergence, latent heat release, vertical transport of heat and water vapor, thereby depicting the multi-scale interactions of low-level jet and meso-scale convective systems in heavy rainfall suitably, and improving the prediction of heavy rainfall in South China in the AFRS as a result.

Development and Validation of a Simple Frozen SoilParameterization Scheme Used for Climate Model

A simple frozen soil parameterization scheme is developed based on NCAR LSM and the effects of re-vised scheme are investigated using Former Soviet Union (FSU) 6 stations measurement data. In the revised model, soil ice content and the energy change in phase change process is considered; the original soil thermal conductivity scheme is replaced by Johanson scheme and the soil thermal and hydraulic properties is modi-fied depending on soil ice content. The comparison of original model with revised model results indicates that the frozen soil scheme can reasonably simulate the energy budget in soil column and the variation of thermal and hydraulic properties as the soil ice content changes. Soil moisture in spring is decreased because of the reduction of infiltration and increment of runoff. Consequently, the partition of heat flux and surface temperature changes correspondingly.

An Evaluation of the Effects of Cloud Parameterization in the R42L9 GCM

Cloud is one of the uncertainty factors influencing the performance of a general circulation model (GCM). Recently, the State Key Laboratory of Atmospheric Sciences and Geophysical Fluid Dynamics, Institute of Atmospheric Physics (LASG/IAP) has developed a new version of a GCM (R42L9). In this work, roles of cloud parameterization in the R42L9 are evaluated through a comparison between two 20-year simulations using different cloud schemes. One scheme is that the cloud in the model is diagnosed from relative humidity and vertical velocity, and the other one is that diagnostic cloud is replaced by retrieved cloud amount from the International Satellite Cloud Climatology Project (ISCCP), combined with the amounts of high-, middle-, and low-cloud and heights of the cloud base and top from the NCEP. The boreal winter and summer seasonal means, as well as the annual mean, of the simulated top-of-atmosphere shortwave radiative flux, surface energy fluxes, and precipitation are analyzed in comparison with the observational estimates and NCEP reanalysis data. The results show that the scheme of diagnostic cloud parameterization greatly contributes to model biases of radiative budget and precipitation. When our derived cloud fractions are used to replace the diagnostic cloud amount, the top-of-atmosphere and surface radiation fields are better estimated as well as the spatial pattern of precipitation. The simulations of the regional precipitation, especially over the equatorial Indian Ocean in winter and the Asia-western Pacific region in summer, are obviously improved.