A Land Surface Model(IAP94) for Climate Studies PartI:Formulation and Validation in Off-line Experiments

The IAP (Institute of Atmospheric Physics) land-surface model (IAP94) is described. This model is a comprehensive one with detailed description for the processes of vegetation, snow and soil. Particular attention has been paid to the cases with three water phases in the surface media. On the basis of the mixture theory and the theory of fluid dynamics of porous media, the system of universal conservational equations for water and heat of soil, snow and vegetation canopy has been constructed. On this background, all important factors that may affect the water and heat balance in media can be considered naturally, and each factor and term possess distinct physical meaning. In the computation of water content and temperature, the water phase change and the heat transportation by water flow are taken into account. Moreover, particular attention has been given to the water vapor diffusion in soil for arid or semi-arid cases, and snow compaction. In the treatment of surface turbulent fluxes, the difference between aerodynamic and thermal roughness is taken into account. The aerodynamic roughness of vegetation is calculated as a function of canopy density, height and zero-plane displacement. An extrapolation of log linear and exponential relationship is used when calculating the wind profile within canopy. The model has been validated against field measurements in off-line simulations. The desirable model′s performance leads to the conclusion that the IAP94 is able to reproduce the main physical mechanisms governing the energy and water balances in the global land surface. Part II of the present study will concern the validation in a 3-D experiment coupled with the IAP Two-Level AGCM.

Impact of Spin-up Forcing on Vegetation States Simulated by a Dynamic Global Vegetation Model Coupled with a Land Surface Model

A dynamic global vegetation model （DGVM） coupled with a land surface model （LSM） is generally initialized using a spin-up process to derive a physically-consistent initial condition. Spin-up forcing, which is the atmospheric forcing used to drive the coupled model to equilibrium solutions in the spin-up process, varies across earlier studies. In the present study, the impact of the spin-up forcing in the initialization stage on the fractional coverages （FCs） of plant functional type （PFT） in the subsequent simulation stage are assessed in seven classic climate regions by a modified Community Land Model’s Dynamic Global Vegetation Model （CLM-DGVM）. Results show that the impact of spin-up forcing is considerable in all regions except the tropical rainforest climate region （TR） and the wet temperate climate region （WM）. In the tropical monsoon climate region （TM）, the TR and TM transition region （TR-TM）, the dry temperate climate region （DM）, the highland climate region （H）, and the boreal forest climate region （BF）, where FCs are affected by climate non-negligibly, the discrepancies in initial FCs, which represent long-term cumulative response of vegetation to different climate anomalies, are large. Moreover, the large discrepancies in initial FCs usually decay slowly because there are trees or shrubs in the five regions. The intrinsic growth timescales of FCs for tree PFTs and shrub PFTs are long, and the variation of FCs of tree PFTs or shrub PFTs can affect that of grass PFTs.

Effects of Crop Growth and Development on Land Surface Fluxes

In this study, the Crop Estimation through Resource and Environment Synthesis model （CERES3.0） was coupled into the Biosphere-Atmosphere Transfer Scheme （BATS）, which is called BATS CERES, to represent interactions between the land surface and crop growth processes. The effects of crop growth and development on land surface processes were then studied based on numerical simulations using the land surface models. Six sensitivity experiments by BATS show that the land surface fluxes underwent substantial changes when the leaf area index was changed from 0 to 6 m2 m-2. Numerical experiments for Yucheng and Taoyuan stations reveal that the coupled model could capture not only the responses of crop growth and development to environmental conditions, but also the feedbacks to land surface processes. For quantitative evaluation of the effects of crop growth and development on surface fluxes in China, two numerical experiments were conducted over continental China： one by BATS CERES and one by the original BATS. Comparison of the two runs shows decreases of leaf area index and fractional vegetation cover when incorporating dynamic crops in land surface simulation, which lead to less canopy interception, vegetation transpiration, total evapotranspiration, top soil moisture, and more soil evaporation, surface runoff, and root zone soil moisture. These changes are accompanied by decreasing latent heat flux and increasing sensible heat flux in the cropland region. In addition, the comparison between the simulations and observations proved that incorporating the crop growth and development process into the land surface model could reduce the systematic biases of the simulated leaf area index and top soil moisture, hence improve the simulation of land surface fluxes.

Mapping Near-surface Air Temperature, Pressure, Relative Humidity and Wind Speed over China's Mainland with High Spatiotemporal Resolution

As part of a joint effort to construct an atmospheric forcing dataset for China's Mainland with high spatiotemporal reso- lution, a new approach is proposed to construct gridded near-surface temperature, relative humidity, wind speed and surface pressure with a resolution of 1 km× 1 km. The approach comprises two steps： （1） fit a partial thin-plate smoothing spline with orography and reanalysis data as explanatory variables to ground-based observations for estimating a trend surface; （2） apply a simple kriging procedure to the residual for trend surface correction. The proposed approach is applied to observations collected at approximately 700 stations over China's Mainland. The generated forcing fields are compared with the corresponding components of the National Centers for Environmental Predic- tion （NCEP） Climate Forecast System Reanalysis dataset and the Princeton meteorological forcing dataset. The comparison shows that, both within the station network and within the resolutions of the two gridded datasets, the interpolation errors of the proposed approach are markedly smaller than the two gridded datasets.

Improving the Vegetation Dynamic Simulation in a Land Surface Model by Using a Statistical-dynamic Canopy Interception Scheme

Canopy interception of incident precipitation, as a critical component of a forest＇s water budget, can affect the amount of water available to the soil, and ultimately vegetation distribution and function. In this paper, a statistical-dynamic approach based on leaf area index and statistical canopy interception is used to parameterize the canopy interception process. The statistical-dynamic canopy interception scheme is implemented into the Community Land Model with dynamic global vegetation model （CLM-DGVM） to improve its dynamic vegetation simulation. The simulation for continental China by the land surface model with the new canopy interception scheme shows that the new one reasonably represents the precipitation intercepted by the canopy. Moreover, the new scheme enhances the water availability in the root zone for vegetation growth, especially in the densely vegetated and semi-arid areas, and improves the model＇s performance of potential vegetation simulation.

Case Analyses and Numerical Simulation of Soil ThermalImpacts on Land Surface Energy Budget Based on anOff-Line Land Surface Model

The statistical relationship between soil thermal anomaly and short-term climate change is presented based on a typical case study. Furthermore, possible physical mechanisms behind the relationship are re-vealed through using an off-line land surface model with a reasonable soil thermal forcing at the bottom of the soil layer. In the first experiment, the given heat flux is 5 W m2 at the bottom of the soil layer (in depth of 6.3 m) for 3 months, while only a positive ground temperature anomaly of 0.06°C can be found compared to the control run. The anomaly, however, could reach 0.65°C if the soil thermal conductivity was one order of magnitude larger. It could be even as large as 0.81°C assuming the heat flux at bottom is 10 W m-2. Mean-while, an increase of about 10 W m−2 was detected both for heat flux in soil and sensible heat on land sur-face, which is not neglectable to the short-term climate change. The results show that considerable response in land surface energy budget could be expected when the soil thermal forcing reaches a certain spatial-tem-poral scale. Therefore, land surface models should not ignore the upward heat flux from the bottom of the soil layer. Moreover, integration for a longer period of time and coupled land-atmosphere model are also necessary for the better understanding of this issue.

A New Approach for Parameter Optimization in Land Surface Model

In this study,a new parameter optimization method was used to investigate the expansion of conditional nonlinear optimal perturbation （CNOP） in a land surface model （LSM） using long-term enhanced field observations at Tongyu station in Jilin Province,China,combined with a sophisticated LSM （common land model,CoLM）.Tongyu station is a reference site of the international Coordinated Energy and Water Cycle Observations Project （CEOP） that has studied semiarid regions that have undergone desertification,salination,and degradation since late 1960s.In this study,three key land-surface parameters,namely,soil color,proportion of sand or clay in soil,and leaf-area index were chosen as parameters to be optimized.Our study comprised three experiments：First,a single-parameter optimization was performed,while the second and third experiments performed triple-and six-parameter optimizations,respectively.Notable improvements in simulating sensible heat flux （SH）,latent heat flux （LH）,soil temperature （TS）,and moisture （MS） at shallow layers were achieved using the optimized parameters.The multiple-parameter optimization experiments performed better than the single-parameter experminent.All results demonstrate that the CNOP method can be used to optimize expanded parameters in an LSM.Moreover,clear mathematical meaning,simple design structure,and rapid computability give this method great potential for further application to parameter optimization in LSMs.

Evaluation of the WRF Model with Different Land Surface Schemes: A Drought Event Simulation in Southwest China during 2009–10

The authors examined the performance of version 3.4.1 of the Weather Research and Forecasting Model(WRF) with various land surface schemes in simulating a severe drought event in Southwest China. Five numerical experiments were completed using the Noah land surface scheme, the Pleim-Xiu land surface scheme, the Noah-MP land surface schemes, the Noah- MP scheme with dynamic vegetation, and the Noah-MP scheme with dynamic vegetation and groundwater processes. In general, all the simulations reasonably reproduced the spatial and temporal variations in precipitation, but significant bias was also found, especially for the spatial pattern of simulated precipitation. The WRF simulations with the Noah-MP series land surface schemes performed slightly better than the WRF simulation with the Noah and Pleim-Xiu land surface schemes in reproducing the severe drought events in Southwest China. The leaf area index(LAI) simulated by the different land surface schemes showed significant deviations in Southwest China. The Pleim-Xiu scheme overestimated the value of LAI by a factor of two. The Noah-MP scheme with dynamical vegetation overestimated the magnitude of the annual cycle of the LAI, although the annual mean LAI was close to observations. The simulated LAI showed a long-term lower value from autumn 2009 to spring 2010 relative to normal years. This indicates that the LAI is a potential indictor to monitor drought events.

An off-line simulation of land surface processes over the northern Tibetan Plateau

In order to further understand the land surface processes over the northern Tibetan Plateau, this study produced an off-line simulated examination at the Bujiao site on the northern Tibetan Plateau from June 2002 to April 2004, using the Noah Land Surface Model （Noah LSM） and observed data from the CAMP/Tibet experiment. The observed data were neces- sarily corrected and the number of soil layers in the Noah LSM was changed from 4 to 10 to enable this off-line simulation and analysis. The main conclusions are as follows： the Noah LSM performed well on the northern Tibetan Plateau. The simulated net radiation, upward longwave radiation, and upward shortwave radiation demonstrated the same remarkable annual and seasonal variation as the observed data, especially the upward longwave radiation. The simulated soil temperatures were acceptably close to the observed temperatures, especially in the shallow soil layers. The simulated freezing and melting processes were shown to start from the surface soil layer and spread down to the deep soil layers, but they took longer than the observed processes. However, Noah LSM did not adequately simulate the soil moisture. Therefore, additional high-quality, long-term observations of land surface-atmosphere processes over the Tibetan Plateau will be a key factor in proper adiustments of the model parameters in the future.

The integration of nitrogen dynamics into a land surface model. Part 1: model description and site-scale validation

Nitrogen cycling has profound effects on carbon uptake in the terrestrial ecosystem and the response of the biosphere to climate changes.However,nutrient cycling is not taken into account in most land surface models for climate change.In this study,a nitrogen model,based on nitrogen transformation processes and nitrogen fluxes exchange between the atmosphere and terrestrial ecosystem,was incorporated into the Atmosphere–Vegetation Interaction Model(AVIM)to simulate the carbon cycle under nitrogen limitation.This new model,AVIM-CN,was evaluated against site-scale eddy covariance–based measurements of an alpine meadow located at Damxung station from the FLUXNET 2015 dataset.Results showed that the annual mean gross primary production simulated by AVIM-CN(0.7073 gC m^-2 d^-1)was in better agreement with the corresponding flux data(0.5407 gC m^-2 d^-1)than the original AVIM(1.1403 gC m^-2 d^-1)at Damxung station.Similarly,ecosystem respiration was also down-regulated,from 1.7695 gC m^-2 d^-1 to 1.0572 gC m^-2 d^-1,after the nitrogen processes were introduced,and the latter was closer to the observed vales(0.8034 gC m^-2 d^-1).Overall,the new results were more consistent with the daily time series of carbon and energy fluxes of observations compared to the former version without nitrogen dynamics.A model that does not incorporate the limitation effects of nitrogen nutrient availability will probably overestimate carbon fluxes by about 40%.

Elucidating Dominant Factors Affecting Land Surface Hydrological Simulations of the Community Land Model over China

In order to compare the impacts of the choice of land surface model(LSM)parameterization schemes,meteorological forcing,and land surface parameters on land surface hydrological simulations,and explore to what extent the quality can be improved,a series of experiments with different LSMs,forcing datasets,and parameter datasets concerning soil texture and land cover were conducted.Six simulations are run for the Chinese mainland on 0.1°×0.1°grids from 1979 to 2008,and the simulated monthly soil moisture(SM),evapotranspiration(ET),and snow depth(SD)are then compared and assessed against observations.The results show that the meteorological forcing is the most important factor governing output.Beyond that,SM seems to be also very sensitive to soil texture information;SD is also very sensitive to snow parameterization scheme in the LSM.The Community Land Model version 4.5(CLM4.5),driven by newly developed observation-based regional meteorological forcing and land surface parameters(referred to as CMFD_CLM4.5_NEW),significantly improved the simulations in most cases over the Chinese mainland and its eight basins.It increased the correlation coefficient values from 0.46 to 0.54 for the SM modeling and from 0.54 to 0.67 for the SD simulations,and it decreased the root-mean-square error(RMSE)from 0.093 to 0.085 for the SM simulation and reduced the normalized RMSE from 1.277 to 0.201 for the SD simulations.This study indicates that the offline LSM simulation using a refined LSM driven by newly developed observation-based regional meteorological forcing and land surface parameters can better model reginal land surface hydrological processes.

Simulation of the Asian Monsoon by IAP AGCM Coupled with an Advanced Land Surface Model(IAP94)

In this paper, the global and regional features of the seasonal variation of general circulation, and especially the Asian monsoon simulated by the Institute of Atmospheric Physics Two-level AGCM coupled with a sophisticated land-surface model (IAP94-GCM) are presented and compared with the observation. The comparison is made by using the equilibrium multiyear seasonal cycle climate from a 100-year integration. In the integration sea surface temperature (SST) and sea ice are taken from the observed climatological data (with seasonal variation) because our purpose is to see the improvement of simulation due to the coupling with an advanced land surface model. Overall, the IAP94-GCM provides a reasonably realistic simulation of the interseasonal and intraseasonal climatology of the Asian monsoon and yields an important information that sheds light on the thermal underpinning and the thermodynamics of the seasonal and even multiscale variabilities associated with the Asian summer monsoon.

A Land Surface Model(IAP94)for Climate Studies Part II: Implementation and Preliminary Results of Coupled Model with IAP GCM

The Institute of Atmospheric Physics Land Surface Model (IAP94) has been incorporated into the IAP two-level atmospheric general circulation model (IAP GCM). Global and regional climatology averaged over the last 25 years of 100 year integrations from the IAP GCM with and without IAP94 (“bucket” scheme) is compared. The simulated results are also compared with the reanalysis data. Major findings are: \ \ (1) The IAP GCM simulation without IAP94 has extensive regions of warmer than observed surface air temperatures, while the simulation with IAP94 very much improves the surface air temperature. \ \ (2) The IAP GCM simulation with IAP94 gives improvement of the simulated precipitation pattern and intensity, especially the precipitation of East Asian summer monsoon and its intraseasonal migration of the rainbelts. \ \ (3) In five selected typical regions, for most of the surface variables such as surface air temperature, precipitation, precipitation minus evaporation, net radiation, latent heat flux and sensible heat flux, the IAP GCM with IAP94 provides better simulations.

A COMPARATIVE STUDY OF TWO LAND SURFACE SCHEMES IN WRF MODEL OVER EASTERN CHINA

Two land surface models, Community Land Model (CLM3.5) and NOAH model, have been coupled to the Weather Research and Forecasting (WRF) model and been used to simulate the precipitation, temperature, and circulation fields, respectively, over eastern China in a typical flood year (1998). The purpose of this study is to reveal the effects of land surface changes on regional climate modeling. Comparisons of simulated results and observation data indicate that changes in land surface processes have significant impact on spatial and temporal distribution of precipitation and temperature patterns in eastern China. Coupling of the CLM3.5 to the WRF model (experiment WRF-C) substantially improves the simulation results over eastern China relative to an older version of WRF coupled to the NOAH-LSM (experiment WRF-N). It is found that the simulation of the spatial pattern of summer precipitation in WRF-C is better than in WRF-N. WRF-C also significantly reduces the summer positive bias of surface air temperature, and its simulated surface air temperature matches more closely to observations than WRF-N does, which is associated with lower sensible heat fluxes and higher latent heat fluxes in WRF-C.

Spatio-temporal variation of land surface temperature and temperature lapse rate over mountainous Kashmir Himalaya

In this study, Land Surface Temperature(LST) and its lapse rate over the mountainous Kashmir Himalaya was estimated using MODIS data and correlated with the observed in-situ air temperature(Tair) data. Comparison between the MODIS LST and Tair showed a close agreement with the maximum error of the estimate ±1°C and the correlation coefficient >0.90. Analysis of the LST data from 2002-2012 showed an increasing trend at all the selected locations except at a site located in the southeastern part of Kashmir valley. Using the GTOPO30 DEM, MODIS LST data was used to estimate the actual temperature lapse rate(ATLR) along various transects across Kashmir Himalaya, which showed significant variations in space and time ranging from 0.3°C to 1.2°C per 100 m altitude change. This observation is at variance with the standard temperature lapse rate(STLR) of 0.65°C used universally in most of the hydrological and other land surface models. Snowmelt Runoff Model(SRM) was used to determine the efficacy of using the ATLR for simulating the stream flows in one of the glaciated and snow-covered watersheds in Kashmir. The use of ATLR in the SRM model improved the R2 between the observed and predicted streamflows from 0.92 to 0.97.It is hoped that the operational use of satellite-derived LST and ATLR shall improve the understanding and quantification of various processes related to climate, hydrology and ecosystem in the mountainous and data-scarce Himalaya where the use of temperature and ATLR are critical parameters for understanding various land surface and climate processes.

Applications of a Surface Runoff Model with Horton and Dunne Runoff for VIC

Surface runoff is mainly generated by two mechanisms, infiltration excess (Horton) runoff and saturation excess (Dunne) runoff; and the spatial variability of soil properties, antecedent soil moisture, topography, and rainfall will result in different surface runoff generation mechanisms. For a large area (e.g., a model grid size of a regional climate model or a general circulation model), these runoff generation mechanisms are commonly present at different portions of a grid cell simultaneously. Missing one of the two major runoff generation mechanisms and failing to consider spatial soil variability can result in significant under/over estimation of surface runoff which can directly introduce large errors in soil moisture states over each model grid cell. Therefore, proper modeling of surface runoff is essential to a reasonable representation of feedbacks in a land-atmosphere system. This paper presents a new surface runoff parameterization with the Philip infiltration formulation that dynamically represents both the Horton and Dunne runoff generation mechanisms within a model grid cell. The parameterization takes into account the effects of soil heterogeneity on Horton and Dunne runoff. The new parameterization is implemented into the current version of the hydrologically based Variable Infiltration Capacity (VIC) land surface model and tested over one watershed in Pennsylvania, USA and over the Shiguanhe Basin in the Huaihe Watershed in China. Results show that the new parameterization plays a very important role in partitioning the water budget between surface runoff and soil moisture in the atmosphere-land coupling system, and has potential applications on large hydrological simulations and land-atmospheric interactions. It is further found that the Horton runoff mechanism should be considered within the context of subgrid-scale spatial variability of soil properties and precipitation.

Plant drought tolerance trait is the key parameter in improving the modeling of terrestrial transpiration in arid and semi-arid regions

The prediction of precipitation depends on accurate modeling of terrestrial transpiration.In recent decades,the trait-based plant hydraulic stress scheme has been developed in land surface models,in order to better predict the hydraulic constraint on terrestrial transpiration.However,the role that each plant functional trait plays in the modeling of transpiration remains unknown.The importance of different plant functional traits for modeled transpiration needs to be addressed.Here,the Morris sensitivity analysis method was implemented in the Common Land Model with the plant hydraulic stress scheme(CoLM-P_(50)HS).Traits related to drought tolerance(P_(50);),stomata,and photosynthesis were screened as the most critical from all 17 plant traits.Among 12 FLUXNET sites,the importance of P_(50);,measured by normalized sensitivity scores,increased towards lower precipitation,whereas the importance of stomatal traits and photosynthetic traits decreased towards drier climate conditions.P_(50);was more important than stomatal traits and photosynthetic traits in arid or semi-arid sites,which implies that hydraulic safety strategies are more crucial than plant growth strategies when plants frequently experience drought.Large variation in drought tolerance traits further proved the coexistence of multiple plant strategies of hydraulic safety.Ignoring the variation in drought tolerance traits may potentially bias the modeling of transpiration.More measurements of drought tolerance traits are therefore necessary to help better represent the diversity of plant hydraulic functions.

Subdaily to Seasonal Change of Surface Energy and Water Flux of the Haihe River Basin in China: Noah and Noah-MP Assessment

The land surface processes of the Noah-MP and Noah models are evaluated over four typical landscapes in the Haihe River Basin(HRB) using in-situ observations. The simulated soil temperature and moisture in the two land surface models(LSMs) is consistent with the observation, especially in the rainy season. The models reproduce the mean values and seasonality of the energy fluxes of the croplands, despite the obvious underestimated total evaporation. Noah shows the lower deep soil temperature. The net radiation is well simulated for the diurnal time scale. The daytime latent heat fluxes are always underestimated, while the sensible heat fluxes are overestimated to some degree. Compared with Noah, Noah-MP has improved daily average soil heat flux with diurnal variations. Generally, Noah-MP performs fairly well for different landscapes of the HRB. The simulated cold bias in soil temperature is possibly linked with the parameterized partition of the energy into surface fluxes. Thus, further improvement of these LSMs remains a major challenge.

Preliminary Assessment of the Common Land Model Coupled with the IAP Dynamic Global Vegetation Model

The Common Land Model(CoLM) was coupled with the IAP Dynamic Global Vegetation Model(IAPDGVM), and the performance of this combined CoLMIAP model was evaluated. Offline simulations using both the original Common Land Model(CoLM-LPJ) and CoLM-IAP were conducted. The CoLM-IAP coupled model showed a significant improvement over CoLMLPJ, as the deciduous tree distribution decreased over temperate and boreal regions, while the distribution of evergreen trees increased over the tropics. Some biases in CoLM-LPJ were preserved, including the overestimation of evergreen trees in tropical savanna, the underestimation of boreal evergreen trees, and the absence of boreal shrubs. However, most of these biases did not exist in a further coupled simulation of IAP-DGVM with the Community Land Model(CLM), for which the parameters of IAP-DGVM were optimized. This implies that further improvement is needed to deal with the differences between CoLM and CLM in parameterizations of landbased physical and biochemical processes.

A Multimodel Ensemble-based Kalman Filter for the Retrieval of Soil Moisture Profiles

With the combination of three land surface models （LSMs） and the ensemble Kalman filter （EnKF）, a multimodel EnKF is proposed in which the multimodel background superensemble error covariance matrix is estimated by two different algorithms： the Simple Model Average （SMA） and the Weighted Average Method （WAM）. The two algorithms are tested and compared in terms of their abilities to retrieve the true soil moisture profile by respectively assimilating both synthetically-generated and actual near-surface soil moisture measurements. The results from the synthetic experiment show that the performances of the SMA and WAM algorithms were quite different. The SMA algorithm did not help to improve the estimates of soil moisture at the deep layers, although its performance was not the worst when compared with the results from the single-model EnKF. On the contrary, the results from the WAM algorithm were better than those from any single-model EnKF. The tested results from assimilating the field measurements show that the performance of the two multimodel EnKF algorithms was very stable compared with the single-model EnKF. Although comparisons could only be made at three shallow layers, on average, the performance of the WAM algorithm was still slightly better than that of the SMA algorithm. As a result, the WAM algorithm should be adopted to approximate the multimodel background superensemble error covariance and hence used to estimate soil moisture states at the relatively deep layers.