The Experts below are selected from a list of 30063 Experts worldwide ranked by ideXlab platform
Rene Laprise - One of the best experts on this subject based on the ideXlab platform.
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considerations of domain size and large scale driving for nested regional climate models impact on internal variability and ability at developing small scale details
2012Co-Authors: Rene Laprise, Emilia Paula Diaconescu, Dragana Kornic, Maja Rapaic, Leo Separovic, Martin Leduc, Oumarou Nikiema, Alejandro Di Luca, Adelina Alexandru, Philippe LucaspicherAbstract:The premise of dynamical downscaling is that a high-resolution, nested Regional Climate Model (RCM), driven by large-scale atmospheric fields at its Lateral Boundary, generates fine scales that are dynamically consistent with the large scales. An RCM is hence expected to act as a kind of magnifying glass that will reveal details that could not be resolved on a coarse mesh. The small scales represent the main potential added value of a high-resolution RCM.
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quantification of the Lateral Boundary forcing of a regional climate model using an aging tracer
2008Co-Authors: Philippe Lucaspicher, Daniel Caya, Sebastien Biner, Rene LapriseAbstract:Abstract The present work introduces a new and useful tool to quantify the Lateral Boundary forcing of a regional climate model (RCM). This tool, an aging tracer, computes the time the air parcels spend inside the limited-area domain of an RCM. The aging tracers are initialized to zero when the air parcels enter the domain and grow older during their migrations through the domain with each time step in the integration of the model. This technique was employed in a 10-member ensemble of 10-yr (1980–89) simulations with the Canadian RCM on a large domain covering North America. The residency time is treated and archived as the other simulated meteorological variables, therefore allowing computation of its climate diagnostics. These diagnostics show that the domain-averaged residency time is shorter in winter than in summer as a result of the faster winter atmospheric circulation. The residency time decreases with increasing height above the surface because of the faster atmospheric circulation at high level...
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The impact of Lateral Boundary data errors on the simulated climate of a nested regional climate model
2007Co-Authors: Emilia Paula Diaconescu, Rene Laprise, Laxmi SushamaAbstract:In this study, we investigate the response of a Regional Climate Model (RCM) to errors in the atmospheric data used as Lateral Boundary conditions (LBCs) using a perfect-model framework nick-named the “Big-Brother Experiment” (BBE). The BBE has been designed to evaluate the errors due to the nesting process excluding other model errors. First, a high-resolution (45 km) RCM simulation is made over a large domain. This simulation, called the Perfect Big Brother (PBB), is driven by the National Centres for Environmental Prediction (NCEP) reanalyses; it serves as reference virtual-reality climate to which other RCM runs will be compared. Next, errors of adjustable magnitude are introduced by performing RCM simulations with increasingly larger domains at lower horizontal resolution (90 km mesh). Such simulations with errors typical of today’s Coupled General Circulation Models (CGCM) are called the Imperfect Big-Brother (IBB) simulations. After removing small scales in order to achieve low-resolution typical of today’s CGCMs, they are used as LBCs for driving smaller domain high-resolution RCM runs; these small-domain high-resolution simulations are called Little-Brother (LB) simulations. The difference between the climate statistics of the IBB and those of PBB simulations mimic errors of the driving model. The comparison of climate statistics of the LB to those of the PBB provides an estimate of the errors resulting solely from nesting with imperfect LBCs. The simulations are performed over the East Coast of North America using the Canadian RCM, for five consecutive February months (from 1990 to 1994). It is found that the errors contained in the large scales of the IBB driving data are transmitted to and reproduced with little changes by the LB. In general, the LB restores a great part of the IBB small-scale errors, even if they do not take part in the nesting process. The small scales are seen to improve slightly in regions with important orographic forcing due to the finer resolution of the RCM. However, when the large scales of the driving model have errors, the small scales developed by the LB have errors as well, suggesting that the large scales precondition the small scales. In order to obtain correct small scales, it is necessary to provide the accurate large-scale circulation at the Lateral Boundary of the RCM.
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validation of the nesting technique in a regional climate model and sensitivity tests to the resolution of the Lateral Boundary conditions during summer
2005Co-Authors: Milena Dimitrijevic, Rene LapriseAbstract:The ability of a regional climate model (RCM) to successfully reproduce the fine-scale features of a regional climate during summer is evaluated using an approach nick-named the “Big-Brother Experiment” (BBE). The BBE establishes a reference virtual-reality climate with a RCM applied on a large and high-resolution domain: this simulation is called the Big-Brother (BB) simulation. This reference simulation is then downgraded by filtering small-scale features that are unresolved in today’s global objective analyses. The resulting fields are then used as nesting data to drive the same RCM, which is integrated, at the same high resolution as the BB, only over a sub-area of the larger BB domain, hence, producing the Little-Brother simulation (LB). With the BBE approach, differences between the two simulated climates (BB and LB) can be unambiguously attributed to errors associated with the dynamical downscaling technique, and not to model errors or observational limitations. The current study focuses on the summer over the West Coast of North America. Results of the stationary and transient parts of the fields, decomposed by horizontal scales, are presented for the month of July, for 5 consecutive years (1990–1994). Three degrees of spatial filtering (roughly equivalent to the global spectral resolution of T30, T60 and T360) as well as two update intervals (3 and 6 h) of the Lateral Boundary conditions (LBC) have been employed. This study establishes that the maximum acceptable resolution of driving data for summer is T30, with improved results employing the T60 resolution of LBC. There is little improvement by reducing the time interval from 6 h to 3 h. These results are generally in agreement with previous studies carried out for winter. The good correlation between LB and BB simulations is more difficult to achieve during the summer season, mostly due to weaker control exerted by LBC. Poor correlations are more pronounced for the transient parts than they are for the stationary parts of the fields. This is especially true for the precipitation field, where differences can be attributed to higher temporal variability during the summer due to the presence of convection.
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sensitivity of a regional climate model to the resolution of the Lateral Boundary conditions
2003Co-Authors: Bertrand Denis, Rene Laprise, Daniel CayaAbstract:The sensitivity of a one-way nested regional climate model (RCM) to the spatial and temporal levels of information provided at its Lateral boundaries is studied. To unambiguously address these two issues, a perfect-prognosis approach called the Big-Brother Experiment (BBE) is employed. It consists in first establishing a reference climate simulation (called the Big Brother) over a large domain and then using the simulated data for nesting another RCM (called Little Brother) integrated over a smaller domain. The effect of degrading the resolution of Lateral Boundary conditions (LBC), spatially and temporally, is investigated by comparing the big- and little-brother climate statistics for the total and fine-scale components of the fields, as well as for their stationary and transient components. Within the BBE framework using a 45-km grid-point RCM, it is found that the one-way nesting approach gives satisfactory results for most fields studied when spatial resolutions degraded by up to a factor of 12 are imposed between the nesting data and the Little Brother. For the LBC update interval, 12 h appears to be the upper limit, while little difference is found between update intervals of 3 and 6 h.
Daniel Caya - One of the best experts on this subject based on the ideXlab platform.
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investigation of the sensitivity of water cycle components simulated by the canadian regional climate model to the land surface parameterization the Lateral Boundary data and the internal variability
2009Co-Authors: Biljana Music, Daniel CayaAbstract:Abstract This study investigates the sensitivity of components of the hydrological cycle simulated by the Canadian Regional Climate Model (CRCM) to Lateral Boundary forcing, the complexity of the land surface scheme (LSS), and the internal variability arising from different models’ initial conditions. This evaluation is a contribution to the estimation of the uncertainty associated to regional climate model (RCM) simulations. The analysis was carried out over the period 1961–99 for three North American watersheds, and it looked at climatological seasonal means, mean (climatological) annual cycles, and interanual variability. The three watersheds—the Mississippi, the St. Lawrence, and the Mackenzie River basins—were selected to cover a large range of climate conditions. An evaluation of simulated water budget components with available observations was also included in the analysis. Results indicated that the response of climatological means and annual cycles of water budget components to land surface param...
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quantification of the Lateral Boundary forcing of a regional climate model using an aging tracer
2008Co-Authors: Philippe Lucaspicher, Daniel Caya, Sebastien Biner, Rene LapriseAbstract:Abstract The present work introduces a new and useful tool to quantify the Lateral Boundary forcing of a regional climate model (RCM). This tool, an aging tracer, computes the time the air parcels spend inside the limited-area domain of an RCM. The aging tracers are initialized to zero when the air parcels enter the domain and grow older during their migrations through the domain with each time step in the integration of the model. This technique was employed in a 10-member ensemble of 10-yr (1980–89) simulations with the Canadian RCM on a large domain covering North America. The residency time is treated and archived as the other simulated meteorological variables, therefore allowing computation of its climate diagnostics. These diagnostics show that the domain-averaged residency time is shorter in winter than in summer as a result of the faster winter atmospheric circulation. The residency time decreases with increasing height above the surface because of the faster atmospheric circulation at high level...
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sensitivity of a regional climate model to the resolution of the Lateral Boundary conditions
2003Co-Authors: Bertrand Denis, Rene Laprise, Daniel CayaAbstract:The sensitivity of a one-way nested regional climate model (RCM) to the spatial and temporal levels of information provided at its Lateral boundaries is studied. To unambiguously address these two issues, a perfect-prognosis approach called the Big-Brother Experiment (BBE) is employed. It consists in first establishing a reference climate simulation (called the Big Brother) over a large domain and then using the simulated data for nesting another RCM (called Little Brother) integrated over a smaller domain. The effect of degrading the resolution of Lateral Boundary conditions (LBC), spatially and temporally, is investigated by comparing the big- and little-brother climate statistics for the total and fine-scale components of the fields, as well as for their stationary and transient components. Within the BBE framework using a 45-km grid-point RCM, it is found that the one-way nesting approach gives satisfactory results for most fields studied when spatial resolutions degraded by up to a factor of 12 are imposed between the nesting data and the Little Brother. For the LBC update interval, 12 h appears to be the upper limit, while little difference is found between update intervals of 3 and 6 h.
Ashish Sharma - One of the best experts on this subject based on the ideXlab platform.
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Correcting Lateral Boundary biases in regional climate modelling: the effect of the relaxation zone
2020Co-Authors: Eytan Rocheta, Jason P. Evans, Ashish SharmaAbstract:Regional climate models (RCM) are an important tool for simulating atmospheric information at finer resolutions often of greater relevance to local scale climate change impact assessment studies. The Lateral and lower Boundary conditions, which form the inputs to the RCM downscaling application, are outputs from the global climate model (GCM). These Boundary variables are known to be biased in GCMs, providing the potential to use a statistical approach that corrects these biases before use in downscaling. An array of bias correction techniques have been developed to remove these biases before being used to drive the RCM, but questions remain on their efficacy in terms of the final downscaled output. This study assesses the impact of these bias correction strategies by focussing on how these corrections are translated as one proceeds from the Lateral boundaries into the model interior. Of specific interest is the change in the correction from generation of the Lateral Boundary conditions as well as how correction information moves through the relaxation zone and into the interior of the model. Here we show that bias correction information passing into the regional climate model is limited by interpolations required to generate Lateral Boundary conditions and dominant outflow wind conditions in the boundaries. This work suggests that these limitations should be addressed in order for bias correction of Lateral Boundary conditions to robustly influence RCM simulations of climate in the interior of the model domain.
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can bias correction of regional climate model Lateral Boundary conditions improve low frequency rainfall variability
2017Co-Authors: Eytan Rocheta, Jason P. Evans, Ashish SharmaAbstract:AbstractGlobal climate model simulations inherently contain multiple biases that, when used as Boundary conditions for regional climate models, have the potential to produce poor downscaled simulations. Removing these biases before downscaling can potentially improve regional climate change impact assessment. In particular, reducing the low-frequency variability biases in atmospheric variables as well as modeled rainfall is important for hydrological impact assessment, predominantly for the improved simulation of floods and droughts. The impact of this bias in the Lateral Boundary conditions driving the dynamical downscaling has not been explored before. Here the use of three approaches for correcting the Lateral Boundary biases including mean, variance, and modification of sample moments through the use of a nested bias correction (NBC) method that corrects for low-frequency variability bias is investigated. These corrections are implemented at the 6-hourly time scale on the global climate model simulati...
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impact of bias corrected reanalysis derived Lateral Boundary conditions on wrf simulations
2017Co-Authors: D B Moalafhi, Ashish Sharma, Jason P. Evans, R Mehrotra, Eytan RochetaAbstract:Lateral and lower Boundary conditions derived from a suitable global reanalysis dataset forms the basis for deriving a dynamically consistent finer resolution downscaled product for climate and hydrological assessment studies. A problem with this, however, is that systematic biases have been noted to be present in the global reanalysis datasets that form these boundaries, biases which can be carried into the downscaled simulations thereby reducing their accuracy or efficacy. In this work, three Weather Research and Forecasting (WRF) model downscaling experiments are undertaken to investigate the impact of bias correcting European Centre for Medium range Weather Forecasting Reanalysis ERA-Interim (ERA-I) atmospheric temperature and relative humidity using Atmospheric Infrared Sounder (AIRS) satellite data. The downscaling is performed over a domain centered over southern Africa between the years 2003 and 2012. The sample mean and the mean as well as standard deviation at each grid cell for each variable are used for bias correction. The resultant WRF simulations of near-surface temperature and precipitation are evaluated seasonally and annually against global gridded observational datasets and compared with ERA-I reanalysis driving field. The study reveals inconsistencies between the impact of the bias correction prior to downscaling and the resultant model simulations after downscaling. Mean and standard deviation bias corrected WRF simulations are, however, found to be marginally better than mean only bias corrected WRF simulations and raw ERA-I reanalysis driven WRF simulations. Performances, however, differ when assessing different attributes in the downscaled field. This raises questions about the efficacy of the correction procedures adopted.
Nadia Pinardi - One of the best experts on this subject based on the ideXlab platform.
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a nested atlantic mediterranean sea general circulation model for operational forecasting
2009Co-Authors: Paolo Oddo, Nadia Pinardi, Mario Adani, Claudia Fratianni, Marina Tonani, D PettenuzzoAbstract:Abstract. A new numerical general circulation ocean model for the Mediterranean Sea has been implemented nested within an Atlantic general circulation model within the framework of the Marine Environment and Security for the European Area project (MERSEA, Desaubies, 2006). A 4-year twin experiment was carried out from January 2004 to December 2007 with two different models to evaluate the impact on the Mediterranean Sea circulation of open Lateral Boundary conditions in the Atlantic Ocean. One model considers a closed Lateral Boundary in a large Atlantic box and the other is nested in the same box in a global ocean circulation model. Impact was observed comparing the two simulations with independent observations: ARGO for temperature and salinity profiles and tide gauges and along-track satellite observations for the sea surface height. The improvement in the nested Atlantic-Mediterranean model with respect to the closed one is particularly evident in the salinity characteristics of the Modified Atlantic Water and in the Mediterranean sea level seasonal variability.
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Lateral open Boundary conditions for nested limited area models a scale selective approach
2008Co-Authors: Paolo Oddo, Nadia PinardiAbstract:Abstract This paper reviews current approaches to the Lateral open Boundary condition problem for nested regional primitive equation ocean numerical models and proposes a new approach that considers a scale decomposition of the nesting field variables for the barotropic Lateral velocity Boundary conditions. The Flather [Flather, R.A., 1976. A tidal model of the north-west European continental shelf. Memories de la Societe Royale des Sciences de Liege 6 (10):141–164] open Lateral Boundary condition is derived from mass conservation considerations and we use this approach to derive a generalized Lateral open Boundary condition for barotropic velocities. In addition we do a scale selective decomposition of the generalized Flather obtaining new and general Lateral scale dependent Boundary conditions. The performance of the new Lateral Boundary conditions have been evaluated in two kinds of experiments: (1) idealized and (2) realistic frameworks. In the idealized framework, as well as the realistic case, the results confirms that the scale selective open Boundary conditions improves the solution almost everywhere but in particular in the shallow depth parts of the model domain. In the realistic case the assessment is more difficult and it is connected also to the capability of the nesting and nested model to reproduce the dynamics contained in the observations.
Eytan Rocheta - One of the best experts on this subject based on the ideXlab platform.
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Correcting Lateral Boundary biases in regional climate modelling: the effect of the relaxation zone
2020Co-Authors: Eytan Rocheta, Jason P. Evans, Ashish SharmaAbstract:Regional climate models (RCM) are an important tool for simulating atmospheric information at finer resolutions often of greater relevance to local scale climate change impact assessment studies. The Lateral and lower Boundary conditions, which form the inputs to the RCM downscaling application, are outputs from the global climate model (GCM). These Boundary variables are known to be biased in GCMs, providing the potential to use a statistical approach that corrects these biases before use in downscaling. An array of bias correction techniques have been developed to remove these biases before being used to drive the RCM, but questions remain on their efficacy in terms of the final downscaled output. This study assesses the impact of these bias correction strategies by focussing on how these corrections are translated as one proceeds from the Lateral boundaries into the model interior. Of specific interest is the change in the correction from generation of the Lateral Boundary conditions as well as how correction information moves through the relaxation zone and into the interior of the model. Here we show that bias correction information passing into the regional climate model is limited by interpolations required to generate Lateral Boundary conditions and dominant outflow wind conditions in the boundaries. This work suggests that these limitations should be addressed in order for bias correction of Lateral Boundary conditions to robustly influence RCM simulations of climate in the interior of the model domain.
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can bias correction of regional climate model Lateral Boundary conditions improve low frequency rainfall variability
2017Co-Authors: Eytan Rocheta, Jason P. Evans, Ashish SharmaAbstract:AbstractGlobal climate model simulations inherently contain multiple biases that, when used as Boundary conditions for regional climate models, have the potential to produce poor downscaled simulations. Removing these biases before downscaling can potentially improve regional climate change impact assessment. In particular, reducing the low-frequency variability biases in atmospheric variables as well as modeled rainfall is important for hydrological impact assessment, predominantly for the improved simulation of floods and droughts. The impact of this bias in the Lateral Boundary conditions driving the dynamical downscaling has not been explored before. Here the use of three approaches for correcting the Lateral Boundary biases including mean, variance, and modification of sample moments through the use of a nested bias correction (NBC) method that corrects for low-frequency variability bias is investigated. These corrections are implemented at the 6-hourly time scale on the global climate model simulati...
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impact of bias corrected reanalysis derived Lateral Boundary conditions on wrf simulations
2017Co-Authors: D B Moalafhi, Ashish Sharma, Jason P. Evans, R Mehrotra, Eytan RochetaAbstract:Lateral and lower Boundary conditions derived from a suitable global reanalysis dataset forms the basis for deriving a dynamically consistent finer resolution downscaled product for climate and hydrological assessment studies. A problem with this, however, is that systematic biases have been noted to be present in the global reanalysis datasets that form these boundaries, biases which can be carried into the downscaled simulations thereby reducing their accuracy or efficacy. In this work, three Weather Research and Forecasting (WRF) model downscaling experiments are undertaken to investigate the impact of bias correcting European Centre for Medium range Weather Forecasting Reanalysis ERA-Interim (ERA-I) atmospheric temperature and relative humidity using Atmospheric Infrared Sounder (AIRS) satellite data. The downscaling is performed over a domain centered over southern Africa between the years 2003 and 2012. The sample mean and the mean as well as standard deviation at each grid cell for each variable are used for bias correction. The resultant WRF simulations of near-surface temperature and precipitation are evaluated seasonally and annually against global gridded observational datasets and compared with ERA-I reanalysis driving field. The study reveals inconsistencies between the impact of the bias correction prior to downscaling and the resultant model simulations after downscaling. Mean and standard deviation bias corrected WRF simulations are, however, found to be marginally better than mean only bias corrected WRF simulations and raw ERA-I reanalysis driven WRF simulations. Performances, however, differ when assessing different attributes in the downscaled field. This raises questions about the efficacy of the correction procedures adopted.
