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- Author or Editor: J.-F. Mahfouf x
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Abstract
This paper presents a simple parameterization of gravitational drainage for land surface schemes describing soil water transfers according to the force-restore method of Deardorff. A one-year time series of observed soil moisture period from HAPEX-MOBILHY (Hydrological Atmospheric Pilot Experiment-Mobilisation du Bilan Hydrique) 1986 revealed the importance of subsurface drainage during the wintertime period. This physical process is accounted for through a Newtonian restore to field capacity when soil moisture is above it. Simulation of the annual cycle of soil moisture by the land surface scheme ISBA (interactions soil biosphere atmosphere) is in this way greatly improved.
Abstract
This paper presents a simple parameterization of gravitational drainage for land surface schemes describing soil water transfers according to the force-restore method of Deardorff. A one-year time series of observed soil moisture period from HAPEX-MOBILHY (Hydrological Atmospheric Pilot Experiment-Mobilisation du Bilan Hydrique) 1986 revealed the importance of subsurface drainage during the wintertime period. This physical process is accounted for through a Newtonian restore to field capacity when soil moisture is above it. Simulation of the annual cycle of soil moisture by the land surface scheme ISBA (interactions soil biosphere atmosphere) is in this way greatly improved.
Abstract
Vajious formulations of surface evaporation are tested against in situ data collected over a plot of loamy bare ground. Numerical simulations lasting seven days are compared with observations of near-surface water content and cumulative evaporation.
A comparison of classical bulk aerodynamic formulations shows similar predictions of daytime evaporation while significant differences are exhibited during the night. The so-called “surface moisture availability method” seems to overestimate the nocturnal evaporation flux.
In the context of this dataset, threshold methods strongly underestimate surface evaporation during the whole period of observations. A sensitivity analysis reveals that threshold evaporation (maximum sustainable water flux) is highly sensitive upon the depth of the top soil layer.
Abstract
Vajious formulations of surface evaporation are tested against in situ data collected over a plot of loamy bare ground. Numerical simulations lasting seven days are compared with observations of near-surface water content and cumulative evaporation.
A comparison of classical bulk aerodynamic formulations shows similar predictions of daytime evaporation while significant differences are exhibited during the night. The so-called “surface moisture availability method” seems to overestimate the nocturnal evaporation flux.
In the context of this dataset, threshold methods strongly underestimate surface evaporation during the whole period of observations. A sensitivity analysis reveals that threshold evaporation (maximum sustainable water flux) is highly sensitive upon the depth of the top soil layer.
Abstract
A sequential assimilation technique based upon optimum interpolation is developed to initialize soil moisture in atmospheric models. Soil moisture increments are linearly related to forecast errors of near-surface atmospheric temperature and relative humidity. Part I has shown that soil moisture can be estimated from surface characteristics (vegetation coverage, soil texture). In this part, the behavior of the method is examined within a three-dimensional mesoscale model. The model includes a realistic land surface parameterization that relates soil moisture to atmospheric variables. Results reveal that after 48-h assimilations soil moisture has converged near reference values by blending atmospheric quantities in the algorithm. The convergence rate is almost independent of the first guess. Sensitivity studies show that the observational errors modulate the efficiency of the process and that results with an analytic formulation of the optimum coefficients are close to those obtained with a Monte Carlo method. These conclusions are of practical interest for an implementation in operational models.
Abstract
A sequential assimilation technique based upon optimum interpolation is developed to initialize soil moisture in atmospheric models. Soil moisture increments are linearly related to forecast errors of near-surface atmospheric temperature and relative humidity. Part I has shown that soil moisture can be estimated from surface characteristics (vegetation coverage, soil texture). In this part, the behavior of the method is examined within a three-dimensional mesoscale model. The model includes a realistic land surface parameterization that relates soil moisture to atmospheric variables. Results reveal that after 48-h assimilations soil moisture has converged near reference values by blending atmospheric quantities in the algorithm. The convergence rate is almost independent of the first guess. Sensitivity studies show that the observational errors modulate the efficiency of the process and that results with an analytic formulation of the optimum coefficients are close to those obtained with a Monte Carlo method. These conclusions are of practical interest for an implementation in operational models.
Abstract
This paper and its companion report on the development of a sequential assimilation technique based upon optimum interpolation in order to initialize soil moisture in atmospheric models. A previous study by Mahfouf has demonstrated that it is possible to estimate soil moisture from the evolution of atmospheric temperature and relative humidity near the surface. The main purpose of this paper is to examine more precisely the dependence of atmospheric low-level parameters upon soil moisture and how this dependence is affected by various factors (soil characteristics, vegetation type, low-level wind). The sensitivity of atmospheric parameters to soil moisture is expressed as the statistical quantities of the optimal interpolation. The importance of observation errors, which define the relevance of the atmospheric parameters for the assimilation procedure, is also investigated. An analytical formulation of the optimal interpolation coefficients is proposed. Finally, the usefulness and limitations of this work for soil moisture analysis in three-dimensional models are discussed.
Abstract
This paper and its companion report on the development of a sequential assimilation technique based upon optimum interpolation in order to initialize soil moisture in atmospheric models. A previous study by Mahfouf has demonstrated that it is possible to estimate soil moisture from the evolution of atmospheric temperature and relative humidity near the surface. The main purpose of this paper is to examine more precisely the dependence of atmospheric low-level parameters upon soil moisture and how this dependence is affected by various factors (soil characteristics, vegetation type, low-level wind). The sensitivity of atmospheric parameters to soil moisture is expressed as the statistical quantities of the optimal interpolation. The importance of observation errors, which define the relevance of the atmospheric parameters for the assimilation procedure, is also investigated. An analytical formulation of the optimal interpolation coefficients is proposed. Finally, the usefulness and limitations of this work for soil moisture analysis in three-dimensional models are discussed.
Abstract
The aim of this study is to test a land data assimilation prototype for the production of a global daily root-zone soil moisture analysis. This system can assimilate microwave L-band satellite observations such as those from the future Hydros NASA mission. The experiments are considered in the framework of the Interaction Soil Biosphere Atmosphere (ISBA) land surface scheme used operationally at the Meteorological Service of Canada for regional and global weather forecasting. A land surface reference state is obtained after a 1-yr global land surface simulation, forced by near-surface atmospheric fields provided by the Global Soil Wetness Project, second initiative (GSWP-2). A radiative transfer model is applied to simulate the microwave L-band passive emission from the surface. The generated brightness temperature observations are distributed in space and time according to the satellite trajectory specified by the Hydros mission. The impact of uncertainties related to the satellite observations, the land surface, and microwave emission models is investigated. A global daily root-zone soil moisture analysis is produced with a simplified variational scheme. The applicability and performance of the system are evaluated in a data assimilation cycle in which the L-band simulated observations, generated from a land surface reference state, are assimilated to correct a prescribed initial root-zone soil moisture error. The analysis convergence is satisfactory in both summer and winter cases. In summer, when considering a 3-K observation error, 90% of land surface converges toward the reference state with a soil moisture accuracy better than 0.04 m3 m−3 after a 4-week assimilation cycle. A 5-K observation error introduces 1-week delay in the convergence. A study of the analysis error statistics is performed for understanding the properties of the system. Special features associated with the interactions between soil water and soil ice, and the presence of soil moisture vertical gradients, are examined.
Abstract
The aim of this study is to test a land data assimilation prototype for the production of a global daily root-zone soil moisture analysis. This system can assimilate microwave L-band satellite observations such as those from the future Hydros NASA mission. The experiments are considered in the framework of the Interaction Soil Biosphere Atmosphere (ISBA) land surface scheme used operationally at the Meteorological Service of Canada for regional and global weather forecasting. A land surface reference state is obtained after a 1-yr global land surface simulation, forced by near-surface atmospheric fields provided by the Global Soil Wetness Project, second initiative (GSWP-2). A radiative transfer model is applied to simulate the microwave L-band passive emission from the surface. The generated brightness temperature observations are distributed in space and time according to the satellite trajectory specified by the Hydros mission. The impact of uncertainties related to the satellite observations, the land surface, and microwave emission models is investigated. A global daily root-zone soil moisture analysis is produced with a simplified variational scheme. The applicability and performance of the system are evaluated in a data assimilation cycle in which the L-band simulated observations, generated from a land surface reference state, are assimilated to correct a prescribed initial root-zone soil moisture error. The analysis convergence is satisfactory in both summer and winter cases. In summer, when considering a 3-K observation error, 90% of land surface converges toward the reference state with a soil moisture accuracy better than 0.04 m3 m−3 after a 4-week assimilation cycle. A 5-K observation error introduces 1-week delay in the convergence. A study of the analysis error statistics is performed for understanding the properties of the system. Special features associated with the interactions between soil water and soil ice, and the presence of soil moisture vertical gradients, are examined.
Abstract
A Canadian Land Data Assimilation System (CaLDAS) for the analysis of land surface prognostic variables is designed and implemented at the Meteorological Service of Canada for the initialization of numerical weather prediction and climate models. The assimilation of different data sources for the production of daily soil moisture and temperature analyses is investigated in a set of observing system simulation experiments over North America. A simplified variational technique is adapted to accommodate different observation types at their appropriate time in a 24-h time window. The screen-level observations of temperature and relative humidity, from conventional synoptic surface observations (SYNOP)/aviation routine weather report (METAR)/surface aviation observation (SA) reports, are considered together with presently available satellite observations provided by the Aqua satellite (microwave C-band), Geostationary Operational Environmental Satellite (GOES) [infrared (IR)], and observations available in the future by the Soil Moisture and Ocean Salinity (SMOS) satellite mission (microwave L-band). The aim of these experiments is to assess the information content brought by each observation type in the land surface analysis. The observation systems are simulated according to their spatial coverage, temporal availability, and nominal or expected errors. The results show that the observable with the largest dynamical response to perturbations of the control variable carries the greatest information content into the analysis. The observational error and the observation frequency counterbalance this feature in the analysis.
If one considers a single observation both for soil moisture and soil temperature analysis, then satellite measurements (L-band, C-band, and IR in decreasing order of importance) are the primary source of information. When observation availability is considered and the highest temporal frequency of screen-level observations is used (1 h), a large amount of information is extracted from SYNOP-like reports. The screen-level observations are shown to provide valuable soil moisture information mainly during the daytime, while during nighttime these observations (and particularly screen-level temperature) are mostly useful for the soil temperature analysis. The results are presented with perspectives for future operational developments and preliminary assimilation experiments are performed with hourly screen-level observations.
Abstract
A Canadian Land Data Assimilation System (CaLDAS) for the analysis of land surface prognostic variables is designed and implemented at the Meteorological Service of Canada for the initialization of numerical weather prediction and climate models. The assimilation of different data sources for the production of daily soil moisture and temperature analyses is investigated in a set of observing system simulation experiments over North America. A simplified variational technique is adapted to accommodate different observation types at their appropriate time in a 24-h time window. The screen-level observations of temperature and relative humidity, from conventional synoptic surface observations (SYNOP)/aviation routine weather report (METAR)/surface aviation observation (SA) reports, are considered together with presently available satellite observations provided by the Aqua satellite (microwave C-band), Geostationary Operational Environmental Satellite (GOES) [infrared (IR)], and observations available in the future by the Soil Moisture and Ocean Salinity (SMOS) satellite mission (microwave L-band). The aim of these experiments is to assess the information content brought by each observation type in the land surface analysis. The observation systems are simulated according to their spatial coverage, temporal availability, and nominal or expected errors. The results show that the observable with the largest dynamical response to perturbations of the control variable carries the greatest information content into the analysis. The observational error and the observation frequency counterbalance this feature in the analysis.
If one considers a single observation both for soil moisture and soil temperature analysis, then satellite measurements (L-band, C-band, and IR in decreasing order of importance) are the primary source of information. When observation availability is considered and the highest temporal frequency of screen-level observations is used (1 h), a large amount of information is extracted from SYNOP-like reports. The screen-level observations are shown to provide valuable soil moisture information mainly during the daytime, while during nighttime these observations (and particularly screen-level temperature) are mostly useful for the soil temperature analysis. The results are presented with perspectives for future operational developments and preliminary assimilation experiments are performed with hourly screen-level observations.
Abstract
Currently, satellite radiances in the Canadian Meteorological Centre operational data assimilation system are only assimilated in clear skies. A two-step method, developed at the European Centre for Medium-Range Weather Forecasts, is considered to assimilate Special Sensor Microwave Imager (SSM/I) observations in rainy atmospheres. The first step consists of a one-dimensional variational data assimilation (1DVAR) method. Model temperature and humidity profiles are adjusted by assimilating either SSM/I brightness temperatures or retrieved surface rain rates (derived from SSM/I brightness temperatures). In the second step, 1DVAR column-integrated water vapor analyses are assimilated in four-dimensional variational data assimilation (4DVAR). At the Meteorological Service of Canada, such a 1DVAR assimilation system has been developed. Model profiles are obtained from a research version of the Global Environmental Multi-Scale model. Several issues raised while developing the 1DVAR system are addressed. The impact of the size of the observation error is studied when brightness temperatures are assimilated. For two case studies, analyses are derived when either surface rain rate or brightness temperatures are assimilated. Differences in the analyzed fields between these configurations are discussed and shortcomings of each approach are identified. Results of sensitivity studies are also provided. First the impact of observation error correlation between channels is investigated. Second, the size of the background temperature error is varied to assess its impact on the analyzed column-integrated water vapor. Third, the importance of each moist physical scheme is investigated. Finally, the portability of moist physical schemes specifically developed for data assimilation is discussed.
Abstract
Currently, satellite radiances in the Canadian Meteorological Centre operational data assimilation system are only assimilated in clear skies. A two-step method, developed at the European Centre for Medium-Range Weather Forecasts, is considered to assimilate Special Sensor Microwave Imager (SSM/I) observations in rainy atmospheres. The first step consists of a one-dimensional variational data assimilation (1DVAR) method. Model temperature and humidity profiles are adjusted by assimilating either SSM/I brightness temperatures or retrieved surface rain rates (derived from SSM/I brightness temperatures). In the second step, 1DVAR column-integrated water vapor analyses are assimilated in four-dimensional variational data assimilation (4DVAR). At the Meteorological Service of Canada, such a 1DVAR assimilation system has been developed. Model profiles are obtained from a research version of the Global Environmental Multi-Scale model. Several issues raised while developing the 1DVAR system are addressed. The impact of the size of the observation error is studied when brightness temperatures are assimilated. For two case studies, analyses are derived when either surface rain rate or brightness temperatures are assimilated. Differences in the analyzed fields between these configurations are discussed and shortcomings of each approach are identified. Results of sensitivity studies are also provided. First the impact of observation error correlation between channels is investigated. Second, the size of the background temperature error is varied to assess its impact on the analyzed column-integrated water vapor. Third, the importance of each moist physical scheme is investigated. Finally, the portability of moist physical schemes specifically developed for data assimilation is discussed.
Abstract
The production of climate simulations using global coupled ocean–atmosphere models at high resolution is currently limited by computational expense and the long periods of integration that are necessary. A method of increasing the number of experiments that can be performed is the so-called time-slice technique. Using the Arpège-climat atmospheric model three 5-yr integrations of this type were run: a control and two integrations forced with sea surface temperatures derived from coupled model simulations of the transient response to increasing carbon dioxide. These coupled models are the ECHAM1 model of the Max-Planck Institute (Hamburg, Germany) and the U.K. Meteorological Office model of the Hadley Centre. The sensitivity of the response to the oceanic forcing is studied. The results are compared with the 10-yr mean atmospheric response of the coupled models at the time of the doubling of CO2. Global warmings ranging from 1.3 K to 1.9 K are obtained. Special attention is given to the modifications that occur in the hydrological cycle and their sensitivity to the SSTs. Climatic signals related to oceanic forcing, such as the modification of the ITCZ maximum of precipitation, are separated from signals due to the internal feedbacks and physical parameterizations of the models.
Abstract
The production of climate simulations using global coupled ocean–atmosphere models at high resolution is currently limited by computational expense and the long periods of integration that are necessary. A method of increasing the number of experiments that can be performed is the so-called time-slice technique. Using the Arpège-climat atmospheric model three 5-yr integrations of this type were run: a control and two integrations forced with sea surface temperatures derived from coupled model simulations of the transient response to increasing carbon dioxide. These coupled models are the ECHAM1 model of the Max-Planck Institute (Hamburg, Germany) and the U.K. Meteorological Office model of the Hadley Centre. The sensitivity of the response to the oceanic forcing is studied. The results are compared with the 10-yr mean atmospheric response of the coupled models at the time of the doubling of CO2. Global warmings ranging from 1.3 K to 1.9 K are obtained. Special attention is given to the modifications that occur in the hydrological cycle and their sensitivity to the SSTs. Climatic signals related to oceanic forcing, such as the modification of the ITCZ maximum of precipitation, are separated from signals due to the internal feedbacks and physical parameterizations of the models.
Abstract
This paper describes recent developments in climate modeling at Météo-France related to land surface processes. The implementation of a simple land surface parameterization, Interactions between Soil Biosphere Atmosphere (ISBA), has gained from previous validations and calibrations at local scale against field datasets and from aggregation procedures devised to define effective land surface properties. Specific improvements for climate purposes are introduced: spatial variability of convective rainfall in canopy drainage estimation and subsurface gravitational percolation. The methodology used to derive climatological maps of land surface parameters at the grid-scale resolution of the model from existing database for soil and vegetation types at global scale is described. A 3-yr integration for the present day climate with a T42L30 version of the climate model has been performed. Results obtained compare favorably with available observed climatologies related to the various components of the continental surface energy and water budgets. Differences are due mostly to a poor simulation of the precipitation field. However, some differences suggest specific improvements in the surface scheme concerning representation of the bare soil albedo, the surface runoff, and the soil moisture initialization. As a first step prior to tropical deforestation experiments presented in Part II, regional analyses over the Amazon forest indicate that the modeled evaporation and net radiation are in good agreement with data collected during the Amazon Region Micrometeorological Experiment campaign.
Abstract
This paper describes recent developments in climate modeling at Météo-France related to land surface processes. The implementation of a simple land surface parameterization, Interactions between Soil Biosphere Atmosphere (ISBA), has gained from previous validations and calibrations at local scale against field datasets and from aggregation procedures devised to define effective land surface properties. Specific improvements for climate purposes are introduced: spatial variability of convective rainfall in canopy drainage estimation and subsurface gravitational percolation. The methodology used to derive climatological maps of land surface parameters at the grid-scale resolution of the model from existing database for soil and vegetation types at global scale is described. A 3-yr integration for the present day climate with a T42L30 version of the climate model has been performed. Results obtained compare favorably with available observed climatologies related to the various components of the continental surface energy and water budgets. Differences are due mostly to a poor simulation of the precipitation field. However, some differences suggest specific improvements in the surface scheme concerning representation of the bare soil albedo, the surface runoff, and the soil moisture initialization. As a first step prior to tropical deforestation experiments presented in Part II, regional analyses over the Amazon forest indicate that the modeled evaporation and net radiation are in good agreement with data collected during the Amazon Region Micrometeorological Experiment campaign.
Abstract
Various parameterizations of the planetary boundary layer (PBL) currently used in three-dimensional (3D) mesoscale models are compared with a more complex scheme including a turbulent kinetic energy (TKE) equation. In the first set of simulations made with a ID model against the classical Wangara data, the mean wind, temperature and moisture calculated in the PBL are nearly insensitive to the choice of the parameterization. In the second set of simulations, the TKE parameterization is used in a 3D mesoscale model to simulate sea breeze flows over south Florida. A comparison is presented with previous simulations of Pielke, and Pielke and Mahrer, for the mean flow, and with the third-order turbulence closure model of Brière for the turbulent variables, including a discussion of the turbulent energy budget, The analysis of the results obtained with the TKE scheme shows that the predicted turbulent fields are qualitatively realistic and interact significantly with the sea breeze circulation. Finally, a comparison is made between the TKE scheme and the simpler parameterization of Pielke and Mahrer. It shows only slight differences as far as the mesoscale structure of the mean variables is concerned.
Abstract
Various parameterizations of the planetary boundary layer (PBL) currently used in three-dimensional (3D) mesoscale models are compared with a more complex scheme including a turbulent kinetic energy (TKE) equation. In the first set of simulations made with a ID model against the classical Wangara data, the mean wind, temperature and moisture calculated in the PBL are nearly insensitive to the choice of the parameterization. In the second set of simulations, the TKE parameterization is used in a 3D mesoscale model to simulate sea breeze flows over south Florida. A comparison is presented with previous simulations of Pielke, and Pielke and Mahrer, for the mean flow, and with the third-order turbulence closure model of Brière for the turbulent variables, including a discussion of the turbulent energy budget, The analysis of the results obtained with the TKE scheme shows that the predicted turbulent fields are qualitatively realistic and interact significantly with the sea breeze circulation. Finally, a comparison is made between the TKE scheme and the simpler parameterization of Pielke and Mahrer. It shows only slight differences as far as the mesoscale structure of the mean variables is concerned.
