Land Surface-atmosphere Interactions Research Articles

Soil Moisture Retrieval from Active Spaceborne Microwave Observations: An Evaluation of Current Techniques

The importance of land surface-atmosphere interactions, principally the effects of soil moisture, on hydrological, meteorological, and ecological processes has gained widespread recognition over recent decades. Its high spatial and temporal variability however, makes soil moisture a difficult parameter to measure and monitor effectively using traditional methods. Microwave remote sensing technology has demonstrated the potential to map and monitor relative soil moisture changes over large areas at regular intervals in time and also the opportunity of measuring, through inverse modelling, absolute soil moisture values. This ability has been demonstrated under a variety of topographic and land cover conditions using both active and passive microwave instruments. The purpose of this paper is to review the current status of soil moisture determination from active microwave remote sensing systems and to highlight the key areas of research that will have to be addressed to achieve routine use of the proposed retrieval approaches.

Analysis of surface and root-zone soil moisture dynamics with ERS scatterometer and the hydrometeorological model SAFRAN-ISBA-MODCOU at Grand Morin watershed (France)

Abstract. Spatial and temporal variations of soil moisture strongly affect flooding, erosion, solute transport and vegetation productivity. Its characterization, offers an avenue to improve our understanding of complex land surface-atmosphere interactions. In this paper, soil moisture dynamics at soil surface (first centimeters) and root-zone (up to 1.5 m depth) are investigated at three spatial scales: local scale (field measurements), 8×8 km2 (hydrological model) and 25×25 km2 scale (ERS scatterometer) in a French watershed. This study points out the quality of surface and root-zone soil moisture data for SIM model and ERS scatterometer for a three year period. Surface soil moisture is highly variable because is more influenced by atmospheric conditions (rain, wind and solar radiation), and presents RMSE up to 0.08 m3 m−3. On the other hand, root-zone moisture presents lower variability with small RMSE (between 0.02 and 0.06 m3 m−3). These results will contribute to satellite and model verification of moisture, but also to better application of radar data for data assimilation in future.

On the relevance of using artificial neural networks for estimating soil moisture content

Summary Soil moisture is a key variable that defines the land surface-atmosphere (boundary layer) interactions, by contributing directly to the surface energy balance and water balance. This paper investigates the utility of the widely adopted data-driven model, namely artificial neural networks (ANNs), for modeling the complex soil moisture dynamics. Datasets from three experimental soil covers (D1, D2, and D3), with thickness of 0.50 m, 0.35 m, and 1.0 m, comprising a thin layer of peat mineral mix over varying thickness of till, are considered in this study. Volumetric soil moisture contents at both the peat and the till layers were modeled as a function of precipitation, air temperature, net radiation, and ground temperature at different layers. Initial simulations illustrated that, in the absence of time-lagged meteorological variables, the ground temperature is the most influential state variable for characterizing the soil moisture, highlighting the strong link between the soil thermal properties and the corresponding moisture status. With the objective of extracting the maximum information from the most influential state variables (ground temperature), a higher-order neural networks (HONNs) model was developed to characterize the soil moisture dynamics. The HONNs resulted in relatively higher correlation coefficient, than traditional ANNs, for some of the soil moisture simulations. Time-lagged inputs were used to improve the model performance and obtain optimum results. The ANN models performed better than a previously developed conceptual model for estimating the depth-averaged soil moisture content. Results from the study indicate that modeling of soil moisture using ANNs is challenging but achievable, and its performance is largely influenced by the structure and formation of the soil covers, which in turn governs the dynamics of soil moisture variability.

CLIMATIC/EDAPHIC CONTROLS ON SOIL CARBON/NITROGEN RESPONSE TO SHRUB ENCROACHMENT IN DESERT GRASSLAND

The proliferation of woody plants in grasslands over the past 100+ years can alter carbon, nitrogen, and water cycles and influence land surface-atmosphere interactions. Although the majority of organic carbon in these ecosystems resides belowground, there is no consensus on how this change in land cover has affected soil organic carbon (SOC) and total nitrogen (TN) pools. The degree to which duration of woody plant occupation, climate, and edaphic conditions have mediated SOC and TN responses to changes in life-form composition are poorly understood. We addressed these issues at a desert grassland site in Arizona, USA, where the leguminous shrub velvet mesquite (Prosopis velutina) has proliferated along an elevation/precipitation/temperature gradient and on contrasting soil morphologic surfaces. On sandy loam complexes of mid-Holocene origin, mean SOC and TN of soils in the grassland matrix increased approximately 68% and approximately 45%, respectively, with increasing elevation. Soil organic carbon pools were comparable and TN pools were approximately 23% higher in Pleistocene-aged clay loam complexes co-occurring with Holocene-aged soils at the upper elevation/climatic zone. Across the site, belowground resources associated with large Prosopis plants were 21-154% (SOC) and 18-127% (TN) higher than those in the grassy matrix. The variance in SOC and TN pools accounted for by Prosopis stem size (a rough surrogate for time of site occupation) was highest at the low- and mid-elevation sites (69-74%) and lowest at the upper elevation site (32-38%). Soil delta15N values ranged from 5.5 per thousand to 6.7 per thousand across the soil/elevation zones but were comparable in herbaceous and shrub-impacted soils and exhibited a weak relationship with Prosopis basal stem diameter (r2 < 0.1) and TN (r2 < 0.08). The SOC delta13C values decreased linearly with increasing Prosopis basal diameter, suggesting that size and isotopic composition of the SOC pool is a function of time of Prosopis site occupation. Isotopic mixture models indicate that encroachment of C3 woody plants has also promoted SOC additions from C4 plant sources, indicative of long-term herbaceous facilitation. Grassy sites in contrasting soil/elevation combinations, initially highly distinctive in their SOC pool size and delta13C, appear to be converging on similar values following approximately 100 years of woody plant proliferation.

Scaling characteristics of spatial patterns of soil moisture from distributed modelling

Characterizing the spatial dynamics of soil moisture fields is a key issue in hydrology, offering an avenue to improve our understanding of complex land surface–atmosphere interactions. In this paper, the statistical structure of soil moisture patterns is examined using modelled soil moisture obtained from the North American Land Data Assimilation System (NLDAS) at 0.125° resolution. The study focuses on the vertically averaged soil moisture in the top 10 cm and 100 cm layers. The two variables display a weak dependence for lower values of surface soil moisture, with the strength of the relationship increasing with the water content of the top layer. In both cases, the variance of the soil moisture follows a power law decay as a function of the averaging area. The superficial layer shows a lower degree of spatial organization and higher temporal variability, which is reflected in rapid changes in time of the slope of the scaling functions of the soil moisture variance. Conversely, the soil moisture in the top 100 cm has lower variability in time and larger spatial correlation. The scaling of these patterns was found to be controlled by the changes in the soil water content. Results have implications for the downscaling of soil moisture to prevent model bias.

Temperature as an Indicator of Landform Influence on Atmospheric Processes

Potential land surface-atmosphere interactions were observed on satellite imagery in the lower reaches of the alluvial valley of the Mississippi River. Monthly temperature data were used to determine if a measurable relationship between the land surface and resulting atmospheric processes can be similarly found in the long-term record throughout the entire year. Comparison of temperatures inside the valley with those outside the valley revealed that annual average minimum temperature in the valley is warmer by 1°C than outside the valley. Analyses of seasonal temperature showed that average maximum temperature in the valley is warmer by 0.5°C in summer and colder by 0.5°C in winter. Average minimum temperature in the valley is consistently over 1°C warmer in all seasons. Monthly temperature analyses showed that average maximum temperature in the valley is colder from November-March and warmer from May-October, while average minimum temperature is consistently warmer in the valley in all months. Average temperature range in the valley is consistently smaller than outside the valley. Statistically significant results indicate that days are hotter in summer and colder in winter, and that nights are warmer all year in the valley. Soil type differences and consequently differing soil moisture regimes are suggested as possible causal mechanisms.

Estimation of Land Surface Heat Fluxes over the Tibetan Plateau Using GMS Data

Abstract A Surface Energy Balance System (SEBS) originally developed for the NOAA Advanced Very High Resolution Radiometer was applied to Geostationary Meteorological Satellite (GMS)-5 Visible/Infrared Spin-Scan Radiometer data that were supplemented with other meteorological data. GMS-5, which is a geostationary satellite, recorded continuous hourly information. Surface temperatures obtained from the GMS-5 data were entered into SEBS to estimate the hourly regional distribution of the surface heat fluxes over the Tibetan Plateau. The estimated fluxes are verified by using corresponding field observations. The diurnal cycle of estimated fluxes agreed well with the field measurements. For example, the diurnal range of the estimated sensible heat flux decreases from June to August. This reflects the change of dry to wet surface characteristics resulting from frequent precipitation during the summer monsoon. Over the Tibetan Plateau, the diurnal range of the surface temperature is as large as the annual range, so that the resultant sensible heat flux has a large diurnal variation. Thus, the hourly estimation based on the GMS data may contribute to a better understanding of the land surface–atmosphere interaction in this critical area.

Energy and water vapour exchanges over a mixedwood boreal forest in Ontario, Canada

AbstractMeasurements of energy, carbon dioxide and water vapour fluxes and supporting meteorological variables were made from 1 September 2003 to 31 August 2004 in a 74‐year‐old mixedwood forest in northern Ontario, Canada, using the eddy covariance technique. Land surface cover data analysis showed that although this forest is comprised of deciduous and coniferous species, their proportion throughout the forest was almost equal, resulting in a homogeneously mixedwood stand. The seasonal pattern of albedo showed a distinct pattern as a result of mixed deciduous and coniferous species with the first low radiation reflection period in early spring (between snowmelt and deciduous leaf‐out), and the second low reflection period in late autumn (between senescence and snowfall). Root zone soil water content remained above 0·32 throughout the growing season, which is characteristic of wet soil conditions. The evaporative flux exceeded the sensible heat flux during most of the growing season. Daily mean evapotranspiration values during the peak growing season were about 2–2·5 mm day−1 with maximum values reaching up to 4–5 mm day−1 on sunny days. The total water loss over the 12‐month measurement period was 480 ± 30 mm, while the total precipitation was 835 mm. Despite high soil water content, bulk surface conductance calculated using the inverted Penman–Monteith formulation, showed a strong correlation with vapour pressure deficit (VPD), indicating stomatal control on water loss during the afternoons with high atmospheric demand and during occasional dry periods. Bulk surface conductance values in this mixedwood forest were relatively higher than those in other boreal coniferous forests and were more similar to values observed in the southern boreal deciduous forests. The Priestley–Taylor α values showed a wide range but during most of the growing season its values were close to unity, indicating that water stress does not play a major role in the overall water loss from this boreal forest ecosystem, while energy supply has a strong control on evapotranspiration. These results will help parameterize accurately energy and water exchanges for the moist mixedwood forests in land surface‐atmosphere interaction and hydrologic models. Copyright © 2006 John Wiley &amp; Sons, Ltd.

Toward a Unified View of the American Monsoon Systems

Abstract An important goal of the Climate Variability and Predictability (CLIVAR) research on the American monsoon systems is to determine the sources and limits of predictability of warm season precipitation, with emphasis on weekly to interannual time scales. This paper reviews recent progress in the understanding of the American monsoon systems and identifies some of the future challenges that remain to improve warm season climate prediction. Much of the recent progress is derived from complementary international programs in North and South America, namely, the North American Monsoon Experiment (NAME) and the Monsoon Experiment South America (MESA), with the following common objectives: 1) to understand the key components of the American monsoon systems and their variability, 2) to determine the role of these systems in the global water cycle, 3) to improve observational datasets, and 4) to improve simulation and monthly-to-seasonal prediction of the monsoons and regional water resources. Among the recent observational advances highlighted in this paper are new insights into moisture transport processes, description of the structure and variability of the South American low-level jet, and resolution of the diurnal cycle of precipitation in the core monsoon regions. NAME and MESA are also driving major efforts in model development and hydrologic applications. Incorporated into the postfield phases of these projects are assessments of atmosphere–land surface interactions and model-based climate predictability experiments. As CLIVAR research on American monsoon systems evolves, a unified view of the climatic processes modulating continental warm season precipitation is beginning to emerge.

1948–98 U.S. Hydrological Reanalysis by the Noah Land Data Assimilation System

Abstract Land surface variables, such as soil moisture, are among the most important components of memory for the climate system. A more accurate and long time series of land surface data is very important for real-time drought monitoring, for understanding land surface–atmosphere interaction, and for improving weather and climate prediction. Thus, the ultimate goal of the present work is to produce a long-term “land reanalysis” with 1) retrospective and 2) real-time update components that are both generated in a manner that remains temporally homogeneous throughout the record. As the first step of the above goal, the retrospective component is reported here. Specifically, a 51-yr (1948–98) set of hourly land surface meteorological forcing is produced and used to execute the Noah land surface model, all on the 1/8° grid of the North American Land Data Assimilation System (NLDAS). The surface forcing includes air temperature, air humidity, surface pressure, wind speed, and surface downward shortwave and longwave radiation, all derived from the National Centers for Environmental Prediction–National Center For Atmospheric Research (NCEP–NCAR) Global Reanalysis. Additionally, a newly improved precipitation analysis is used to provide realistic hourly precipitation forcing on the NLDAS grid. Some unique procedures are described and applied to yield retroactive forcing that is temporally homogeneous over the 51 yr at the spatial and temporal resolution, including a terrain height adjustment that accounts for the terrain differences between the global reanalysis and the NLDAS. The land model parameters and fixed fields are derived from existing high-resolution datasets of vegetation, soil, and orography. The land reanalysis output from the Noah land surface model consists of eight energy balance components and skin temperature, which are output at 3-hourly intervals, and 15 other variables (i.e., water balance components, surface state variables, etc.), which are output at daily intervals for the period of 1 January 1948 through 31 December 1998. Using soil moisture observations throughout Illinois over 1984–98 as validation, an improvement in the simulated soil moisture (of the Noah model versus a forerunner leaky bucket model) is illustrated in terms of an improved annual cycle (much better phasing) and somewhat higher anomaly correlation for the anomalies, especially in central and southern Illinois. Nonetheless, considerable room for model improvement remains. For example, the simulated anomalies are overly uniform in the vertical compared to the observations, and some likely routes for model improvement in this aspect are proposed.

A downscaling method for distributing surface soil moisture within a microwave pixel: Application to the Monsoon '90 data

A downscaling method for the near-surface soil moisture retrieved from passive microwave sensors is applied to the PBMR data collected during the Monsoon '90 experiment. The downscaling method requires (1) the coarse resolution microwave observations, (2) the fine-scale distribution of soil temperature and (3) the fine-scale distribution of surface conditions composed of atmospheric forcing and the parameters involved in the modeling of land surface–atmosphere interactions. During the Monsoon '90 experiment, eight ground-based meteorological and flux stations were operating over the 150 km 2 study area simultaneously with the acquisition of the aircraft-based L-band PBMR data. The heterogeneous scene is hence composed of eight subpixels and the microwave pixel is generated by aggregating the microwave emission of all sites. The results indicate a good agreement between the downscaled and ground-based soil moisture as long as the intensity of solar radiation is sufficiently high to use the soil temperature as a tracer of the spatial variability of near-surface soil moisture.

A combined modeling and multispectral/multiresolution remote sensing approach for disaggregation of surface soil moisture: application to SMOS configuration

A new physically based disaggregation method is developed to improve the spatial resolution of the surface soil moisture extracted from the Soil Moisture and Ocean Salinity (SMOS) data. The approach combines the 40-km resolution SMOS multiangular brightness temperatures and 1-km resolution auxiliary data composed of visible, near-infrared, and thermal infrared remote sensing data and all the surface variables involved in the modeling of land surface-atmosphere interaction available at this scale (soil texture, atmospheric forcing, etc.). The method successively estimates a relative spatial distribution of soil moisture with fine-scale auxiliary data, and normalizes this distribution at SMOS resolution with SMOS data. The main assumption relies on the relationship between the radiometric soil temperature inverted from the thermal infrared and the microwave soil moisture. Based on synthetic data generated with a land surface model, it is shown that the radiometric soil temperature can be used as a tracer of the spatial variability of the 0-5 cm soil moisture. A sensitivity analysis shows that the algorithm remains stable for big uncertainties in auxiliary data and that the uncertainty in SMOS observation seems to be the limiting factor. Finally, a simple application to the SGP97/AVHRR data illustrates the usefulness of the approach.

Impact of land–surface processes on the interannual variability of tropical climate in the LMD GCM

Tropical monsoon circulations exhibit substantial interannual variability. Establishing clear links between this variability and the slowly varying boundary forcing (sea surface temperatures, SSTs, and land surface conditions) has proved difficult. For example, no clear relationships have been found between SST anomalies associated with El Nino/La Nina events and monsoon rainfall. Despite much research over the past 50 years, there are still questions regarding how different components of the land–atmosphere–ocean system contribute to tropical monsoon variability. This study examines the question of land-surface–atmosphere interactions in large-scale tropical convection and their role in rainfall interannual variability. The analysis method is based on a conceptual model of convection energetics applied every day of the simulation at the grid points within the region of interest. This allows for a distinction between the frequency and the characteristic energy and water cycle of these events. With two ensembles of five and three experiments in which different land-surface schemes are used, the relation between land-surface processes and variation of the frequency of convection is studied. It has been found in this modeling study that the formulation of land surface schemes may be important for both the simulation of mean tropical precipitation and its interannual variability by way of the frequency of convective events. Linked to this is an increased response of hydrological cycle over land to SSTAs. Numerous studies have suggested that large-scale factors, such as SST, are the dominant control. However the influence of surface processes depends on the areal extent and distance that separates the region from the ocean. The fact that differences between tropical regions decreases as convection intensifies strengthens this hypothesis. The conclusion is that it is inappropriate to separate the causes of interannual variability between SSTAs and land-surface anomalies to explain precipitation variations as land surface processes play a significant mediating role in the relationship between SSTs and monsoon strength. However there remains the possibility that a substantial portion of variability is due to dynamical processes internal to the atmosphere. Determining the relative roles of internal and lower boundary forcing processes in producing interannual variations in the tropical climate is a major objective of future research.

Remote sensing parameterization of the processes of energy and water cycle over desertification areas

In order to understand the processes of land surface-atmosphere interaction over desertification area, it is indispensable to utilize of satellite remote sensing. Two scenes of Landsat TM were used to produce a set of maps of surface reflectance, MSAVI, vegetation coverage, surface temperature, net radiation, soil heat flux, sensible heat flux and latent heat flux. Statistical analysis based on these maps revealed some quantitative significant land surface characteristics. Future developments of the method are also discussed.

Assimilating remote sensing data in a surface flux–soil moisture model

AbstractA key state variable in land surface–atmosphere interactions is soil moisture, which affects surface energy fluxes, runoff and the radiation balance. Soil moisture modelling relies on parameter estimates that are inadequately measured at the necessarily fine model scales. Hence, model soil moisture estimates are imperfect and often drift away from reality through simulation time. Because of its spatial and temporal nature, remote sensing holds great promise for soil moisture estimation. Much success has been attained in recent years in soil moisture estimation using passive and active microwave sensors, but progress has been slow. One reason for this is the scale disparity between remote sensing data resolution and the hydrologic process scale. Other impediments include vegetation cover and microwave penetration depth. As a result, currently there is no comprehensive method for assimilating remote soil moisture observations within a surface hydrology model at watershed or larger scales.This paper describes a measurement–modelling system for estimating the three‐dimensional soil moisture distribution, incorporating remote microwave observations, a surface flux–soil moisture model, a radiative transfer model and Kalman filtering. The surface model, driven by meteorological observations, estimates the vertical and lateral distribution of water. Based on the model soil moisture profiles, microwave brightness temperatures are estimated using the radiative transfer model. A Kalman filter is then applied using modelled and observed brightness temperatures to update the model soil moisture profile.The modelling system has been applied using data from the Southern Great Plains 1997 field experiment. In the presence of highly inaccurate rainfall input, assimilation of remote microwave data results in better agreement with observed soil moisture. Without assimilation, it was seen that the model near‐surface soil moisture reached a minimum that was higher than observed, resulting in substantial errors during very dry conditions. Updating moisture profiles daily with remotely sensed brightness temperatures reduced but did not eliminate this bias. Copyright © 2002 John Wiley &amp; Sons, Ltd.

Does Soil Moisture Influence Climate Variability and Predictability over Australia?

Interannual variations of Australian climate are strongly linked to the El Nino-Southern Oscillation (ENSO) phenomenon. However, the impact of other mechanisms on prediction, such as atmosphere-land surface interactions, has been less frequently investigated. Here, the impact of soil moisture variability on interannual climate variability and predictability is examined using the Bureau of Meteorology Research Centre atmospheric general circulation model. Two sets of experiments are run, each with five different initial conditions. In the first set of experiments, soil moisture is free to vary in response to atmospheric forcing in each experiment according to a set of simple prognostic equations. A potential predictability index is computed as the ratio of the model's internal variability to its external forced variability. This estimates the level of predictability obtained assuming perfect knowledge of future ocean surface temperatures. A second set of five experiments with prescribed soil moisture is performed. A comparison between these two sets of experiments reveals that fluctuations of soil moisture increase the persistence, the variance, and the potential predictability of surface temperature and rainfall. The interrelationship between these two variables is also strongly dependent upon the soil water content. Results are particularly marked over Australia in this model. A novel feature of this study is the focus on the effectiveness of ENSO-based statistical seasonal forecasting over Australia. Forecasting skill is shown to be crucially dependent upon soil moisture variability over the continent. In fact, surface temperature forecasts in this manner are not possible without soil moisture variability. This result suggests that a better representation of land-surface interaction has the potential to increase the skill of seasonal prediction schemes.

Comparison of surface meteorological variables from TOVS and AVHRR

This paper examines, on global and continental spatial scales, the relationship between surface meteorological observations retrieved from the Advanced Very High Resolution Radiometer (AVHRR) and the TIROS Operational Vertical Sounder (TOVS). Comparisons of spatial and temporal patterns of surface temperature, air temperature, and vapor pressure deficit were carried out using data acquired between January 1989 and December 1990 on a monthly time step. These variables play an important role in hydrological, ecological, and land surface–atmosphere interactions. Additional comparisons were done for seven large regions (ca. 10 6 km 2) centered on the continental land surface areas in order to test for regional differences and to reduce the effect of coastal transition zones on spatial resampling. The mean statistical differences between the TOVS and AVHRR estimates were 5 °C for surface temperature, 4 °C for air temperature, and 1.5 kPa for vapor pressure deficit. In addition to long-term and spatially extensive ground data sets, these comparisons provide independent validation of different estimation methods using various satellite sensor measurements resampled to comparable spatial scales. Favorable comparison of the two sensors considered here provide greater confidence in the use of these data sets for global monitoring and modeling applications.

A Statistical–Dynamical Parameterization of Interception and Land Surface–Atmosphere Interactions

At a local scale, the interception capacity of the canopy depends on a variety of climatic and canopy factors. Of particular importance is the intensity of rainfall—interception capacity varies inversely with rainfall intensity. At a field or regional scale, like the scale of global climate models, the spatially averaged interception also depends significantly on the spatial variability of rainfall intensity and total precipitation depth. A new parameterization of canopy interception is developed. In the new parameterization, the spatial average of actual interception is obtained as a function of rainfall intensity and total precipitation depth, and of an interception capacity, which depends on the characteristics of the leaf surface and of the vegetation cover. In a statistical‐dynamical framework, the new parameterization also accounts for the subgrid-scale spatial variability of rainfall intensity and total precipitation depth. The implications of accounting for the dependence of interception capacity on rainfall characteristics are examined by assessing the consequent responses of the energy and the water fluxes at the land surface. This is accomplished by incorporating the new parameterization into a soil‐plant‐atmosphere column model that is fundamentally based on the physical parameterizations of NCAR’s Community Climate Model.

Desertification, and climate change: the case for greater convergence

Poor knowledge of links between desertification and globalclimate change is limiting funding from the Global Environment Facility foranti-desertification projects and realization of synergies between theConvention to Combat Desertification (CCD) and the FrameworkConvention on Climate Change (FCCC). Greater convergence betweenresearch in the two fields could overcome these limitations, improve ourknowledge of desertification, and benefit four areas of global climate changestudies: mitigation assessment; accounting for land cover change in thecarbon budget; land surface-atmosphere interactions; and climate changeimpact forecasting. Convergence would be assisted if desertification weretreated more as a special case in dry areas of the global process of landdegradation, and stimulated by: (a) closer cooperation between the FCCCand CCD; (b) better informal networking between desertification and globalclimate change scientists, e.g. within the framework of theIntergovernmental Panel on Climate Change (IPCC). Both strategies wouldbe facilitated if the FCCC and CCD requested the IPCC to provide ascientific framework for realizing the synergies between them.

Meso-scale effects of tropical deforestation in Amazonia: preparatory LBA modelling studies

Abstract. As part of the preparation for the Large-Scale Biosphere Atmosphere Experiment in Amazonia, a meso-scale modelling study was executed to highlight deficiencies in the current understanding of land surface atmosphere interaction at local to sub-continental scales in the dry season. Meso-scale models were run in 1-D and 3-D mode for the area of Rondonia State, Brazil. The important conclusions are that without calibration it is difficult to model the energy partitioning of pasture; modelling that of forest is easier due to the absence of a strong moisture deficit signal. The simulation of the boundary layer above forest is good, above deforested areas (pasture) poor. The models' underestimate of the temperature of the boundary layer is likely to be caused by the neglect of the radiative effects of aerosols caused by biomass burning, but other factors such as lack of sufficient entrainment in the model at the mixed layer top may also contribute. The Andes generate patterns of subsidence and gravity waves, the effects of which are felt far into the Rondonian area The results show that the picture presented by GCM modelling studies may need to be balanced by an increased understanding of what happens at the meso-scale. The results are used to identify key measurements for the LBA atmospheric meso-scale campaign needed to improve the model simulations. Similar modelling studies are proposed for the wet season in Rondonia, when convection plays a major role.Key words. Atmospheric composition and structure (aerosols and particles; biosphere-atmosphere interactions) · Meterology and atmospheric dynamics (mesoscale meterology)