Role Of Land Surface Processes Research Articles

Spatio-Temporal Characteristics and Differences in Snow Density between the Tibet Plateau and the Arctic

The Tibet Plateau (TP) and the Arctic are typically cold regions with abundant snow cover, which plays a key role in land surface processes. Knowledge of variations in snow density is essential for understanding hydrology, ecology, and snow cover feedback. Here, we utilized extensive measurements recorded by 697 ground-based snow sites during 1950–2019 to identify the spatio-temporal characteristics of snow density in these two regions. We examined the spatial heterogeneity of snow density for different snow classes, which are from a global seasonal snow cover classification system, with each class determined from air temperature, precipitation, and wind speed climatologies. We also investigated possible mechanisms driving observed snow density differences. The long-term mean snow density in the Arctic was 1.6 times that of the TP. Slight differences were noted in the monthly TP snow densities, with values ranging from 122 ± 29 to 158 ± 52 kg/m3. In the Arctic, however, a clear increasing trend was shown from October to June, particularly with a rate of 30.3 kg/m3 per month from March to June. For the same snow class, the average snow density in the Arctic was higher than that in the TP. The Arctic was characterized mainly by a longer snowfall duration and deeper snow cover, with some areas showing perennial snow cover. In contrast, the TP was dominated by seasonal snow cover that was shallower and warmer, with less (more) snowfall in winter (spring). The results will be helpful for future simulations of snow cover changes and land interactions at high latitudes and altitudes.

Role of land surface processes on Indian summer monsoon rainfall: Understanding and impact assessment

Indian Summer Monsoon is a synoptic-scale atmospheric circulation system manifested by the boundary forcing from both continents and tropical oceans. Unlike oceans, the land surface processes are complex in nature due to the heterogeneities in land surface characteristics and its associated feedbacks, thereby constraining theaccurate representation of the land surface in NWP models. Thus, understanding the land-atmosphere interaction becomes increasingly crucial especially during the Indian summer monsoon season due to the underlying warm and moist surface layer conducive forevapotranspiration, thereby fueling land atmosphere coupling during the season. The representation of surface heterogeneity and variability are constrained due to lack of surface measurements which necessitate development of land surface analysis. The major aimof the present studyisthree-fold;firstly, understanding land surfaces processes associated with the monsoonal rainfall events, secondly, preparation of a state-of-art high-resolution land surface data over India, andfinally, impact assessment of high-resolution land surface initialization on simulation monsoonalrainfall events. This study has implications for developing improved prediction system associated with the Indian Summer Monsoon.
 Indian Summer Monsoon is a synoptic-scale atmospheric circulation system manifested by the boundary forcing from both continents and tropical oceans. Unlike oceans, the land surface processes are complex in nature due to the heterogeneities in land surface characteristics and its associated feedbacks, thereby constraining theaccurate representation of the land surface in NWP models. Thus, understanding the land-atmosphere interaction becomes increasingly crucial especially during the Indian summer monsoon season due to the underlying warm and moist surface layer conducive forevapotranspiration, thereby fueling land atmosphere coupling during the season. The representation of surface heterogeneity and variability are constrained due to lack of surface measurements which necessitate development of land surface analysis. The major aimof the present studyisthree-fold;firstly, understanding land surfaces processes associated with the monsoonal rainfall events, secondly, preparation of a state-of-art high-resolution land surface data over India, andfinally, impact assessment of high-resolution land surface initialization on simulation monsoonalrainfall events. This study has implications for developing improved prediction system associated with the Indian Summer Monsoon.

Integrating eco-evolutionary optimality principle and land processes for evapotranspiration estimation

Evapotranspiration (ET) plays an important role in land surface processes and global climate. ET cannot be observed directly at a large scale, and its spatial distribution is mainly obtained by ET models at present. However, the application of existing ET models is greatly hampered by a number of plant functional type (PFT)-specific empirical parameters and the difficulty in partitioning the contribution of ET from the canopy and soil. To this end, an eco-evolutionary optimality-based evapotranspiration model (EEOET) was developed in this study with some schemes used in the Noah-MP land surface model. The principle of eco-evolutionary optimality expresses the underlying acclimation/adaption of plants to diverse environments and thus greatly reduces the number of PFT-specific parameters. The Noah-MP provides schemes to partition the energy between the canopy and soil and to calculate photosynthetic and ET resistances. Owing to the above, the EEOET model, which has enhanced mechanisms and fewer PFT-specific parameters, requires inputs of daily land surface temperature, vegetation-related data (leaf area index and fractional vegetation), and soil information (soil texture and soil water content) in addition to routine meteorological data. The model output is daily ET. Global evaluation across 74 FLUXNET sites indicates that the model is capable of reasonably estimating ET for all PFTs and is also comparable to existing models that require more tunable parameters. In addition, the EEOET model is less sensitive to uncertainties in the input data than other models, making it a more robust solution for estimating ET under various conditions.

Land–atmosphere interactions in sub-polar and alpine climates in the CORDEX flagship pilot study Land Use and Climate Across Scales (LUCAS) models – Part 1: Evaluation of the snow-albedo effect

Abstract. Seasonal snow cover plays a major role in the climate system of the Northern Hemisphere via its effect on land surface albedo and fluxes. In climate models the parameterization of interactions between snow and atmosphere remains a source of uncertainty and biases in the representation of local and global climate. Here, we evaluate the ability of an ensemble of regional climate models (RCMs) coupled with different land surface models to simulate snow–atmosphere interactions over Europe in winter and spring. We use a previously defined index, the snow-albedo sensitivity index (SASI), to quantify the radiative forcing associated with snow cover anomalies. By comparing RCM-derived SASI values with SASI calculated from reanalyses and satellite retrievals, we show that an accurate simulation of snow cover is essential for correctly reproducing the observed forcing over middle and high latitudes in Europe. The choice of parameterizations, and primarily the choice of the land surface model, strongly influences the representation of SASI as it affects the ability of climate models to simulate snow cover accurately. The degree of agreement between the datasets differs between the accumulation and ablation periods, with the latter one presenting the greatest challenge for the RCMs. Given the dominant role of land surface processes in the simulation of snow cover during the ablation period, the results suggest that, during this time period, the choice of the land surface model is more critical for the representation of SASI than the atmospheric model.

Global spatiotemporally continuous MODIS land surface temperature dataset

Land surface temperature (LST) plays a critical role in land surface processes. However, as one of the effective means for obtaining global LST observations, remote sensing observations are inherently affected by cloud cover, resulting in varying degrees of missing data in satellite-derived LST products. Here, we propose a solution. First, the data interpolating empirical orthogonal functions (DINEOF) method is used to reconstruct invalid LSTs in cloud-contaminated areas into ideal, clear-sky LSTs. Then, a cumulative distribution function (CDF) matching-based method is developed to correct the ideal, clear-sky LSTs to the real LSTs. Experimental results prove that this method can effectively reconstruct missing LST data and guarantee acceptable accuracy in most regions of the world, with RMSEs of 1–2 K and R values of 0.820–0.996 under ideal, clear-sky conditions and RMSEs of 4–7 K and R values of 0.811–0.933 under all weather conditions. Finally, a spatiotemporally continuous MODIS LST dataset at 0.05° latitude/longitude grids is produced based on the above method.

Improving surface soil moisture retrievals through a novel assimilation algorithm to estimate both model and observation errors

Soil moisture controls the land surface water and energy budget and plays a crucial role in land surface processes. Based on certain mathematical rules, data assimilation can merge satellite observations and land surface models, and produce spatiotemporally continuous profile soil moisture. The two mainstream assimilation algorithms (variational-based and sequential-based) both need model error and observation error estimates, which greatly impact the assimilation results. Moreover, the performance of land data assimilation relies heavily on the specification of model parameters. However, it is always challenging to specify these errors and model parameters. In this study, a dual-cycle assimilation algorithm was proposed for addressing the above issue. In the inner cycle, the Ensemble Kalman Filter (EnKF) is run with parameters of both model and observation operators and their errors, which are provided by the outer cycle. Both the analyzed state variable and the innovation are reserved at each analysis moment. In the outer cycle, the innovation time series kept by the inner cycle are fed into a likelihood function to adjust the values of parameters of both the model and observation operators and their errors through an optimization algorithm. A series of assimilation experiments were first performed based on the Lorenz-63 model. The results illustrate that the performance of the dual-cycle algorithm substantially surpasses those of both the classical parameter calibration and the standard EnKF. Subsequently, the Advanced Microwave Scanning Radiometer of earth Observing System (AMSR-E) brightness temperatures were assimilated into the simple biosphere model scheme version 2 (SiB2) with a radiative transfer model as the observation operator in two experimental areas, namely Naqu on the Tibetan Plateau and a Coordinate Enhanced Observing (CEOP) reference site in Mongolia. The results indicate that the dual-cycle assimilation algorithm can simultaneously estimate model parameters, observation operator parameters, model error, and observation error, thus improving surface soil moisture estimation in comparison with other assimilation algorithms. Since the dual-cycle assimilation algorithm can estimate the observation errors, it provides the potential for assimilating multi-source remote sensing data to generate physically consistent land surface state and flux estimates.

Cloudy land surface temperature retrieval from three-channel microwave data

ABSTRACTLand surface temperature (LST) plays an important role in land surface processes, and it is a key input for estimating important hydrological states and fluxes, such as soil moisture and evapotranspiration. In this study, a three-channel method is proposed to retrieve cloudy LST values from passive microwave data based on the relationship among surface emissivities at 18.70, 36.50, and 89.00 GHz over a bare soil surface. The performance of the method was evaluated using simulated data, resulting in a root mean square error (RMSE) of approximately 1.4 K over the bare soil surface. This method was further extended to retrieve cloudy LST values over a natural surface. Due to the lack of in situ LST measurements, ground-based air temperatures were used as proxy data to validate the cloudy LST values retrieved from AMSR-E data. The RMSE values of the differences between the retrieved cloudy LST values and the ground-based air temperatures are approximately 3.4 K and 4.3 K for the descending and ascending overpasses, respectively. The results demonstrate that the three-channel method can be used to retrieve cloudy LST values from passive microwave data with reasonable accuracy.

Estimating soil moisture content using laboratory spectral data

Monitoring soil moisture is important for agriculture and forestry and plays an essential role in land surface processes as well as providing feedback among the earth’s surface ecosystems. Large-scale regional soil moisture spatial data can be obtained with a reliable and operational approach using remote sensing. In this paper, we provide an operational framework for retrieving soil moisture using laboratory spectral data. The inverted Gaussian function was used to fit soil spectral data, and its feature parameters, including absorption depth (AD) and absorption area (AA), were selected as variables for a soil moisture estimate model. There was a significant correlative relationship between soil moisture and AD, as well as AA near 1400 and 1900 nm. A one-variable linear regression model was established to estimate soil moisture. The model was evaluated using the determination coefficients (R2), root mean square error and average precision. Four models were established and evaluated in this study. The determination coefficients of the four models ranged from 0.794 to 0.845. The average accuracy for soil moisture estimates ranged from 90 to 92%. The results prove that it is feasible to estimate soil moisture using remote sensing technology.

Multiscale soil moisture estimates using static and roving cosmic-ray soil moisture sensors

Abstract. Soil moisture plays a critical role in land surface processes and as such there has been a recent increase in the number and resolution of satellite soil moisture observations and the development of land surface process models with ever increasing resolution. Despite these developments, validation and calibration of these products has been limited because of a lack of observations on corresponding scales. A recently developed mobile soil moisture monitoring platform, known as the rover, offers opportunities to overcome this scale issue. This paper describes methods, results and testing of soil moisture estimates produced using rover surveys on a range of scales that are commensurate with model and satellite retrievals. Our investigation involved static cosmic-ray neutron sensors and rover surveys across both broad (36 × 36 km at 9 km resolution) and intensive (10 × 10 km at 1 km resolution) scales in a cropping district in the Mallee region of Victoria, Australia. We describe approaches for converting rover survey neutron counts to soil moisture and discuss the factors controlling soil moisture variability. We use independent gravimetric and modelled soil moisture estimates collected across both space and time to validate rover soil moisture products. Measurements revealed that temporal patterns in soil moisture were preserved through time and regression modelling approaches were utilised to produce time series of property-scale soil moisture which may also have applications in calibration and validation studies or local farm management. Intensive-scale rover surveys produced reliable soil moisture estimates at 1 km resolution while broad-scale surveys produced soil moisture estimates at 9 km resolution. We conclude that the multiscale soil moisture products produced in this study are well suited to future analysis of satellite soil moisture retrievals and finer-scale soil moisture models.

Evaluating common land model energy fluxes using FLUXNET data

Given the crucial role of land surface processes in global and regional climates, there is a pressing need to test and verify the performance of land surface models via comparisons to observations. In this study, the eddy covariance measurements from 20 FLUXNET sites spanning more than 100 site-years were utilized to evaluate the performance of the Common Land Model (CoLM) over different vegetation types in various climate zones. A decomposition method was employed to separate both the observed and simulated energy fluxes, i.e., the sensible heat flux, latent heat flux, net radiation, and ground heat flux, at three timescales ranging from stepwise (30 min) to monthly. A comparison between the simulations and observations indicated that CoLM produced satisfactory simulations of all four energy fluxes, although the different indexes did not exhibit consistent results among the different fluxes. A strong agreement between the simulations and observations was found for the seasonal cycles at the 20 sites, whereas CoLM underestimated the latent heat flux at the sites with distinct dry and wet seasons, which might be associated with its weakness in simulating soil water during the dry season. CoLM cannot explicitly simulate the midday depression of leaf gas exchange, which may explain why CoLM also has a maximum diurnal bias at noon in the summer. Of the eight selected vegetation types analyzed, CoLM performs best for evergreen broadleaf forests and worst for croplands and wetlands.

Exploring new topography-based subgrid spatial structures for improving land surface modeling

Abstract. Topography plays an important role in land surface processes through its influence on atmospheric forcing, soil and vegetation properties, and river network topology and drainage area. Land surface models with a spatial structure that captures spatial heterogeneity, which is directly affected by topography, may improve the representation of land surface processes. Previous studies found that land surface modeling, using subbasins instead of structured grids as computational units, improves the scalability of simulated runoff and streamflow processes. In this study, new land surface spatial structures are explored by further dividing subbasins into subgrid structures based on topographic properties, including surface elevation, slope and aspect. Two methods (local and global) of watershed discretization are applied to derive two types of subgrid structures (geo-located and non-geo-located) over the topographically diverse Columbia River basin in the northwestern United States. In the global method, a fixed elevation classification scheme is used to discretize subbasins. The local method utilizes concepts of hypsometric analysis to discretize each subbasin, using different elevation ranges that also naturally account for slope variations. The relative merits of the two methods and subgrid structures are investigated for their ability to capture topographic heterogeneity and the implications of this on representations of atmospheric forcing and land cover spatial patterns. Results showed that the local method reduces the standard deviation (SD) of subgrid surface elevation in the study domain by 17 to 19 % compared to the global method, highlighting the relative advantages of the local method for capturing subgrid topographic variations. The comparison between the two types of subgrid structures showed that the non-geo-located subgrid structures are more consistent across different area threshold values than the geo-located subgrid structures. Overall the local method and non-geo-located subgrid structures effectively and robustly capture topographic, climatic and vegetation variability, which is important for land surface modeling.

West African monsoon decadal variability and surface-related forcings: Second West African Monsoon Modeling and Evaluation Project Experiment (WAMME II).

The second West African Monsoon Modeling and Evaluation Project Experiment (WAMME II) is designed to improve understanding of the possible roles and feedbacks of sea surface temperature (SST), land use land cover change (LULCC), and aerosols forcings in the Sahel climate system at seasonal to decadal scales. The project's strategy is to apply prescribed observationally based anomaly forcing, i.e., "idealized but realistic" forcing, in simulations by climate models. The goal is to assess these forcings' effects in producing/amplifying seasonal and decadal climate variability in the Sahel between the 1950s and the 1980s, which is selected to characterize the great drought period of the last century. This is the first multi-model experiment specifically designed to simultaneously evaluate such relative contributions. The WAMME II models have consistently demonstrated that SST forcing is a major contributor to the 20th century Sahel drought. Under the influence of the maximum possible SST forcing, the ensemble mean of WAMME II models can produce up to 60% of the precipitation difference during the period. The present paper also addresses the role of SSTs in triggering and maintaining the Sahel drought. In this regard, the consensus of WAMME II models is that both Indian and Pacific Ocean SSTs greatly contributed to the drought, with the former producing an anomalous displacement of the Intertropical Convergence Zone (ITCZ) before the WAM onset, and the latter mainly contributes to the summer WAM drought. The WAMME II models also show that the impact of LULCC forcing on the Sahel climate system is weaker than that of SST forcing, but still of first order magnitude. According to the results, under LULCC forcing the ensemble mean of WAMME II models can produces about 40% of the precipitation difference between the 1980s and the 1950s. The role of land surface processes in responding to and amplifying the drought is also identified. The results suggest that catastrophic consequences are likely to occur in the regional Sahel climate when SST anomalies in individual ocean basins and in land conditions combine synergistically to favor drought.

Recent advances in monsoon studies in China

This review provides a synopsis of the major progress that has been made in monsoon studies in China and to further bridge the gap between the Chinese and international meteorological community. It consists of seven major sections. After the introduction, the second section begins with the global monsoon systems and their seasonal variation, based on some new methods proposed in recent years. Besides, some major intraseasonal features of East Asian monsoon, including the onset of South China Sea summer monsoon are discussed. In the third section, we review the interactions between ENSO and the East Asian monsoon, focusing in particular on the results of Chinese meteorologists that indicate the influence of ENSO on the East Asian summer monsoon (EASM) is obviously different from that on the tropical monsoon. Besides the tropical Pacific, other ocean basins, such as the Indian Ocean and the Atlantic Ocean, are also important to the East Asian monsoon, and this topic is discussed in the fourth section. In the fifth section, we address the role of land surface processes in East Asian monsoon. For example, we describe work that has shown more snow cover in spring on the Tibetan Plateau is followed by a weakened EASM and more summer rainfall in the Yangtze River valleys. The sixth section focuses on the influence of atmospheric circulation in the Southern Hemisphere (SH) on EASM, demonstrating how the signal from the SH is likely to provide new clues for the seasonal forecasting of summer rainfall in China. Finally, in the seventh section, we concentrate on the interdecadal variations of EASM. In particular, we look at a significant interdecadal variation that occurred at the end of the 1970s, and how our understanding of this feature could affect forecasting ability.

Impact of Land Surface Processes on the South Asian Monsoon Simulations Using WRF Modeling System

The Weather Research and Forecasting model has been used to examine the role of land surface processes on Indian summer monsoon simulations. Isolated experiments have been carried out with physical parameterization schemes (land surface and planetary boundary layer) and data assimilation to examine their relative roles in the representation of regional hydroclimate in model simulations. The impact of vegetation green fraction on the model simulations has been extensively studied by replacing the default United States Geological Survey (USGS) vegetation cover data with that of Indian Space Research Organisation (ISRO) data. Results indicate that differences in the treatment of surface processes in the model lead to large differences in precipitation simulation over the Indian domain. Several hydroclimate parameters from the simulations using ISRO and USGS vegetation green fractions were examined. It is seen that the role of vegetation green fraction in these experiments has been to increase latent heat flux to the atmosphere. Two sets of data assimilation experiments were also carried out for an entire year using the same set of observed data but with different land surface parameterization schemes. It is found that evenwhen using the same observed data, the differences in land surface schemes reduce the impact and contribution of observed data being assimilated into the model. The hydroclimate over the region becomes a function of the land surface scheme. This study highlights the importance of vegetation green fraction and land surface schemes in the context of the regional hydroclimate over South Asia.

The role of land-surface processes in modulating the Indian monsoon annual cycle

The annual cycle of solar radiation, together with the resulting land–ocean differential heating, is traditionally considered the dominant forcing controlling the northward progression of the Indian monsoon. This study makes use of a state-of-the-art atmospheric general circulation model in a realistic configuration to conduct “perpetual” experiments aimed at providing new insights into the role of land–atmosphere processes in modulating the annual cycle of precipitation over India. The simulations are carried out at three important stages of the monsoon cycle: March, May, and July. Insolation and SSTs are held fixed at their respective monthly mean values, thus eliminating any external seasonal forcing. In the perpetual May experiment both precipitation and circulation are able to considerably evolve only by regional internal land–atmosphere processes and the mediation of soil hydrology. A large-scale equilibrium state is reached after approximately 270 days, closely resembling mid-summer climatological conditions. As a result, despite the absence of external forcing, intense and widespread rains over India are able to develop in the May-like state. The interaction between soil moisture and circulation, modulated by surface heating over the northwestern semi-arid areas, determines a slow northwestward migration of the monsoon, a crucial feature for the existence of desert regions to the west. This also implies that the land–atmosphere system in May is far from being in equilibrium with the external forcing. The inland migration of the precipitation front comprises a succession of large-scale 35–50 day coupled oscillations between soil moisture, precipitation, and circulation. The oscillatory regime is self-sustained and entirely due to the internal dynamics of the system. In contrast to the May case, minor changes in the land–atmosphere system are found when the model is initialized in March and, more surprisingly, in July, the latter case further emphasizing the role of northwestern surface heating.

Role of land surface processes and diffuse/direct radiation partitioning in simulating the European climate

Abstract. The influence of land processes and in particular of diffuse/direct radiation partitioning on surface fluxes and associated regional-scale climate feedbacks is investigated using ERA-40 driven simulations over Europe performed with the COSMO-CLM2 Regional Climate Model (RCM). Two alternative Land Surface Models (LSMs), a 2nd generation LSM (TERRA_ML) and a more advanced 3rd generation LSM (Community Land Model version 3.5), and two versions of the atmospheric component are tested, as well as a revised coupling procedure allowing for variations in diffuse/direct light partitioning at the surface, and their accounting by the land surface component. Overall, the RCM performance for various variables (e.g., surface fluxes, temperature and precipitation) is improved when using the more advanced 3rd generation LSM. These improvements are of the same order of magnitude as those arising from a new version of the atmospheric component, demonstrating the benefit of using a realistic representation of land surface processes for regional climate simulations. Taking into account the variability in diffuse/direct light partitioning at the surface further improves the model performance in terms of summer temperature variability at the monthly and daily time scales. Comparisons with observations show that the RCM realistically captures temporal variations in diffuse/direct light partitioning as well as the evapotranspiration sensitivity to these variations. Our results suggest that a modest but consistent fraction (up to 3 %) of the overall variability in summer temperature can be explained by variations in the diffuse to direct ratio.

Modeling the Summertime Climate of Southwest Asia: The Role of Land Surface Processes in Shaping the Climate of Semiarid Regions

Abstract Presented is a study on the role of land surface processes in determining the summertime climate over the semiarid region of southwest Asia. In this region, a warm surface air temperature bias of 3.5°C is simulated in the summer by using the standard configuration of Regional Climate Model version 3 (RegCM3). Biases are also simulated in surface albedo (underestimation), shortwave incident radiation (overestimation), and vapor pressure (underestimation). Based on satellite measurements documented in NASA’s surface radiation budget (SRB) dataset, a correction in surface albedo by 4% is introduced in RegCM3 to match the observed SRB data. Increasing albedo values results in a nearly 1°C cooling over the region. In addition, by incorporating RegCM3’s dust module and including subgrid variability for surface wind, shortwave incident radiation bias originally of about 45 W m−2 is reduced by 30 W m−2. As a result, the reduction of shortwave incident radiation cools the surface by 0.6°C. Finally, including a representation for the irrigation and marshlands of Mesopotamia produces surface relative humidity values closer to observations, thus eliminating a nearly 5-mb vapor pressure dry bias over some of the region. Consequently, the representation of irrigation and marshlands results in cooling of nearly 1°C in areas downwind of the actual land-cover change. Along with identified biases in observational datasets, these combined processes explain the 3.5°C warm bias in RegCM3 simulations. Therefore, it is found that accurate representations of surface albedo, dust emissions, and irrigation are important in correctly modeling summertime climates of semiarid regions.

Soil moisture initialization effects in the Indian monsoon system

Abstract. Towards the goal to understand the role of land-surface processes over the Indian sub-continent, a series of soil-moisture sensitivity simulations have been performed using a non-hydrostatic regional climate model COSMO-CLM. The experiments were driven by the lateral boundary conditions provided by the ERA-Interim (ECMWF) reanalysis. The simulation results show that the pre-monsoonal soil moisture has a significant influence on the monsoonal precipitation. Both, positive and negative soil-moisture precipitation (S-P) feedback processes are of importance. The negative S-P feedback process is especially influential in the western and the northern parts of India.

Simulation of the Indian summer monsoon using comprehensive atmosphere–land interactions, in the absence of two-way air–sea interactions

Community Land Model version 2 (CLM2) as a comprehensive land surface model and a simple land surface model (SLM) were coupled to an atmospheric climate model to investigate the role of land surface processes in the development and the persistence of the South Asian summer monsoon. Two-way air-sea interactions were not considered in order to identify the reproducibility of the monsoon evolution by the comprehensive land model, which includes more realistic vertical soil moisture structures, vegetation and 2-way atmosphere-land interactions at hourly intervals. In the monsoon development phase (May and June). comprehensive land-surface treatment improves the representation of atmospheric circulations and the resulting convergence/divergence through the improvements in differential heating patterns and surface energy fluxes. Coupling with CLM2 also improves the timing and spatial distribution of rainfall maxima, reducing the seasonal rainfall overestimation by approx.60 % (1.8 mm/d for SLM, 0.7 mm/dI for CLM2). As for the interannual variation of the simulated rainfall, correlation coefficients of the Indian seasonal rainfall with observation increased from 0.21 (SLM) to 0.45 (CLM2). However, in the mature monsoon phase (July to September), coupling with the CLM2 does not exhibit a clear improvement. In contrast to the development phase, latent heat flux is underestimated and sensible heat flux and surface temperature over India are markedly overestimated. In addition, the moisture fluxes do not correlate well with lower-level atmospheric convergence, yielding correlation coefficients and root mean square errors worse than those produced by coupling with the SLM. A more realistic representation of the surface temperature and energy fluxes is needed to achieve an improved simulation for the mature monsoon period.

Role of land surface parameterizations on modeling cold-pooling events and low-level jets

Land surface parameterization schemes play a significant role in the accuracy of meso-local scale numerical models by accounting for the exchange of energy and water between the soil and the atmosphere. The role of land surface processes during large-scale cold-pooling events was studied with two land surface schemes (LSMs) in the Advanced Research Weather Forecasting model (ARW). Model evaluation was complex due to the surface and boundary layer interactions at different temporal and spatial scales as revealed by a scale dependent variance analysis. Wavelet analysis was used for the first time to analyze the model errors with specific focus on land surface processes. The ARW model was also evaluated for the formation of a low-level jet (LLJ). It is shown that vertical resolution in the model boundary layer played a significant role in determining the characteristics of LLJ, which influenced the lower boundary layer structure and moisture distribution. The results showed that the simulated low-level jet over southern Georgia was sensitive to the land surface parameterization and led to a significant difference in the boundary layer exchange. The jet shear played a crucial role in the maintenance of turbulence and weak shear caused excessive radiative cooling leading to unrealistic cold pools in the model. The results are important for regional downscaling as the excessive cold pools that are simulated in the model can go unnoticed.