Small-scale circulation in regional climate model: the hydrostatic approximation influence

2021 ◽  
Author(s):  
Alexander Titov ◽  
Alexander Khoperskov ◽  
Konstantin Firsov ◽  
Evgeny Baskakov
Keyword(s):  
Regional Climate Model ◽  
Climate Model ◽  
Regional Climate ◽  
Small Scale ◽  
Hydrostatic Approximation

scholarly journals Regional Climate Models Add Value to Global Model Data: A Review and Selected Examples

2011 ◽  
Vol 92 (9) ◽  
pp. 1181-1192 ◽  
Author(s):  
Frauke Feser ◽  
Burkhardt Rockel ◽  
Hans von Storch ◽  
Jörg Winterfeldt ◽  
Matthias Zahn
Keyword(s):  
Regional Climate Model ◽  
Climate Models ◽  
Climate Model ◽  
Dynamical Downscaling ◽  
Spatial Scales ◽  
Regional Climate ◽  
Regional Climate Models ◽  
Small Scale ◽  
Model Data ◽  
Polar Lows

An important challenge in current climate modeling is to realistically describe small-scale weather statistics, such as topographic precipitation and coastal wind patterns, or regional phenomena like polar lows. Global climate models simulate atmospheric processes with increasingly higher resolutions, but still regional climate models have a lot of advantages. They consume less computation time because of their limited simulation area and thereby allow for higher resolution both in time and space as well as for longer integration times. Regional climate models can be used for dynamical down-scaling purposes because their output data can be processed to produce higher resolved atmospheric fields, allowing the representation of small-scale processes and a more detailed description of physiographic details (such as mountain ranges, coastal zones, and details of soil properties). However, does higher resolution add value when compared to global model results? Most studies implicitly assume that dynamical downscaling leads to output fields that are superior to the driving global data, but little work has been carried out to substantiate these expectations. Here a series of articles is reviewed that evaluate the benefit of dynamical downscaling by explicitly comparing results of global and regional climate model data to the observations. These studies show that the regional climate model generally performs better for the medium spatial scales, but not always for the larger spatial scales. Regional models can add value, but only for certain variables and locations—particularly those influenced by regional specifics, such as coasts, or mesoscale dynamics, such as polar lows. Therefore, the decision of whether a regional climate model simulation is required depends crucially on the scientific question being addressed.

scholarly journals Convection-permitting regional climate simulations for representing floods in small and medium sized catchments in the Eastern Alps

2018 ◽  
Author(s):  
Christian Reszler ◽  
Matthew Blasie Switanek ◽  
Heimo Truhetz
Keyword(s):  
Regional Climate Model ◽  
Bias Correction ◽  
Climate Models ◽  
Climate Model ◽  
Flash Flood ◽  
Regional Climate ◽  
Eastern Alps ◽  
Small Scale ◽  
Distributed Hydrological Model ◽  
Flood Generation

Abstract. Small scale floods are a consequence of high precipitation rates in small areas that can occur along frontal activity and convective storms. This situation is expected to become more severe due to a warming climate, when single precipitation events resulting from deep convection become more intense (Super Clausius-Clapeyron effect). Regional climate model (RCM) evaluations and inter-comparisons have shown that there is evidence that an increase in regional climate model resolution and in particular, at the convection permitting scale, will lead to a better representation of the spatial and temporal characteristics of heavy precipitation at small and medium scales. In this paper, the benefit of grid size reduction and bias correction in climate models are evaluated in their ability to properly represent flood generation in small and medium sized catchments. The climate models are coupled with a distributed hydrological model. The study area is the Eastern Alps, where small scale storms often occur along with heterogeneous rainfall distributions leading to a very local flash flood generation. The work is carried out in a small multi-model (ensemble) framework using two different RCMs (CCLM and WRF) in different grid sizes. Bias correction is performed by the use of the novel Scaled Distribution Mapping (SDM) method. The results show, that for small catchments (

scholarly journals A Hybrid Downscaling Approach for Future Temperature and Precipitation Change

2020 ◽  
Vol 59 (11) ◽  
pp. 1793-1807 ◽  
Author(s):  
Helene Birkelund Erlandsen ◽  
Kajsa M. Parding ◽  
Rasmus Benestad ◽  
Abdelkader Mezghani ◽  
Marie Pontoppidan
Keyword(s):  
Regional Climate Model ◽  
Statistical Models ◽  
Large Scale ◽  
Climate Model ◽  
Dynamical Downscaling ◽  
Global Climate ◽  
Global Climate Model ◽  
Regional Climate ◽  
Small Scale ◽  
Regional Climate Model Output

AbstractWe used empirical–statistical downscaling in a pseudoreality context, in which both large-scale predictors and small-scale predictands were based on climate model results. The large-scale conditions were taken from a global climate model, and the small-scale conditions were taken from dynamical downscaling of the same global model with a convection-permitting regional climate model covering southern Norway. This hybrid downscaling approach, a “perfect model”–type experiment, provided 120 years of data under the CMIP5 high-emission scenario. Ample calibration samples made rigorous testing possible, enabling us to evaluate the effect of empirical–statistical model configurations and predictor choices and to assess the stationarity of the statistical models by investigating their sensitivity to different calibration intervals. The skill of the statistical models was evaluated in terms of their ability to reproduce the interannual correlation and long-term trends in seasonal 2-m temperature T2m, wet-day frequency fw, and wet-day mean precipitation μ. We found that different 30-yr calibration intervals often resulted in differing statistical models, depending on the specific choice of years. The hybrid downscaling approach allowed us to emulate seasonal mean regional climate model output with a high spatial resolution (0.05° latitude and 0.1° longitude grid) for up to 100 GCM runs while circumventing the issue of short calibration time, and it provides a robust set of empirically downscaled GCM runs.

Application of Scale-Selective Data Assimilation to Regional Climate Modeling and Prediction

2010 ◽  
Vol 138 (4) ◽  
pp. 1307-1318 ◽  
Author(s):  
Shiqiu Peng ◽  
Lian Xie ◽  
Bin Liu ◽  
Fredrick Semazzi
Keyword(s):  
Data Assimilation ◽  
Regional Climate Model ◽  
Atmospheric Circulation ◽  
Large Scale ◽  
Climate Model ◽  
Regional Climate ◽  
Regional Model ◽  
Global Model ◽  
Small Scale ◽  
Pass Filter

Abstract A method referred to as scale-selective data assimilation (SSDA) is designed to inject the large-scale components of the atmospheric circulation from a global model into a regional model to improve regional climate simulations and predictions. The SSDA is implemented through the following procedure: 1) using a low-pass filter to extract the large-scale components of the atmospheric circulation from global analysis or model forecasts; 2) applying the filter to extract the regional-scale and the large-scale components of the atmospheric circulation from the regional model simulations or forecasts; 3) assimilating the large-scale circulation obtained from the global model into the corresponding component simulated by the regional model using the method of three-dimensional variational data assimilation (3DVAR) while maintaining the small-scale components from the regional model during the assimilation cycle; 4) combining the small-scale and the assimilated large-scale components as the adjusted forecasts by the regional climate model and allowing the two components to mutually adjust outside the data assimilation cycle. A case study of summer 2005 seasonal climate hindcasting for the regions of the Atlantic and the eastern United States indicates that the large-scale components from the Global Forecast System (GFS) analysis can be effectively assimilated into the regional model using the scale-selective data assimilation method devised in this study, resulting in an improvement in the overall results from the regional climate model.

scholarly journals A multivariate comparison of two land-surface models integrated into an Arctic Regional Climate System model

1997 ◽  
Vol 25 ◽  
pp. 127-131
Author(s):  
Amanda Lynch ◽  
David McGinnis ◽  
William L. Chapman ◽  
Jeffrey S. Tilley
Keyword(s):  
Regional Climate Model ◽  
Land Surface ◽  
Climate Model ◽  
Regional Climate ◽  
Climate System ◽  
System Model ◽  
Small Scale ◽  
Land Surface Models ◽  
Climate System Model ◽  
Surface Models

Different vegetation models impact the atmospheric response of a regional climate model in different ways, and hence have an impact upon the ability of that model to match an observed climatology. Using a multivariate principal-component analysis, we investigate the relationships between several land-surface models (BATS, LSM) coupled to a regional climate model, and observed climate parameters over the North Slope of Alaska. In this application, annual cycle simulations at 20 km spatial resolution are compared with European Centre for Medium-Range Weather Forecasts (ECMWF) climatology. Initial results demonstrate broad agreement between all models; however, small-scale regional variations between land-surface models indicate the strengths and weaknesses of the land-surface treatments in a climate system model. Specifically, we found that the greater surface-moisture availability and temperature-dependent albedo formulation of the LSM model allow for a higher proportion of low-level cloud, and a later, more rapid transition from the winter to the summer regime. Crucial to this transition is the seasonal cycle of incoming solar radiation. These preliminary results indicate the importance of the land-surface hydrologic cycle in modelling the seasonal transitions.

scholarly journals A multivariate comparison of two land-surface models integrated into an Arctic Regional Climate System model

1997 ◽  
Vol 25 ◽  
pp. 127-131
Author(s):  
Amanda Lynch ◽  
David McGinnis ◽  
William L. Chapman ◽  
Jeffrey S. Tilley
Keyword(s):  
Regional Climate Model ◽  
Land Surface ◽  
Climate Model ◽  
Regional Climate ◽  
Climate System ◽  
System Model ◽  
Small Scale ◽  
Land Surface Models ◽  
Climate System Model ◽  
Surface Models

Different vegetation models impact the atmospheric response of a regional climate model in different ways, and hence have an impact upon the ability of that model to match an observed climatology. Using a multivariate principal-component analysis, we investigate the relationships between several land-surface models (BATS, LSM) coupled to a regional climate model, and observed climate parameters over the North Slope of Alaska. In this application, annual cycle simulations at 20 km spatial resolution are compared with European Centre for Medium-Range Weather Forecasts (ECMWF) climatology. Initial results demonstrate broad agreement between all models; however, small-scale regional variations between land-surface models indicate the strengths and weaknesses of the land-surface treatments in a climate system model. Specifically, we found that the greater surface-moisture availability and temperature-dependent albedo formulation of the LSM model allow for a higher proportion of low-level cloud, and a later, more rapid transition from the winter to the summer regime. Crucial to this transition is the seasonal cycle of incoming solar radiation. These preliminary results indicate the importance of the land-surface hydrologic cycle in modelling the seasonal transitions.

A Methodology for the Regional-Scale-Decomposed Atmospheric Water Budget: Application to a Simulation of the Canadian Regional Climate Model Nested by NCEP–NCAR Reanalyses over North America

2006 ◽  
Vol 134 (3) ◽  
pp. 854-873 ◽  
Author(s):  
Soline Bielli ◽  
René Laprise
Keyword(s):  
Regional Climate Model ◽  
Large Scale ◽  
Climate Model ◽  
Spatial Scales ◽  
Regional Climate ◽  
Moisture Flux ◽  
Added Value ◽  
Small Scale ◽  
Flux Divergence ◽  
Moisture Flux Divergence

Abstract The purpose of this work is to study the added value of a regional climate model with respect to the global analyses used to drive the regional simulation, with a special emphasis on the nonlinear interactions between different spatial scales, focusing on the moisture flux divergence. The atmospheric water budget is used to apply the spatial-scale decomposition approach, as it is a key factor in the energetics of the climate. A Fourier analysis is performed individually for each field on pressure levels. Each field involved in the computation of moisture flux divergence is separated into three components that represent selected scale bands, using the discrete cosine transform. The divergence of the moisture flux is computed from the filtered fields. Instantaneous and monthly mean fields from a simulation performed with the Canadian Regional Climate Model are decomposed and allowed to separate the added value of the model to the total fields. Results show that the added value resides in the nonlinear interactions between large (greater than 1000 km) and small (smaller than 600 km) scales. The main small-scale forcing of the wind is topographic, whereas the humidity tends to show more small scales over the ocean. The time-mean divergence of moisture flux is also decomposed into contributions from stationary eddies and transient eddies. Both stationary and transient eddies are decomposed into different spatial scales and show very different patterns. The time-mean divergence due to transient eddies is dominated by large-scale (synoptic scale) features with little small scales. The divergence due to stationary eddies is a combination of small- and large-scale terms, and the main small-scale contribution occurs over the topography. The same decomposition has been applied to the NCEP–NCAR reanalyses used to drive the regional simulation; the results show that the model best reproduces the time-fluctuation component of the moisture flux divergence, with a correlation between the model simulation and the NCEP–NCAR reanalyses above 0.90.

scholarly journals Long-term variance of heavy precipitation across central Europe using a large ensemble of regional climate model simulations

2020 ◽  
Vol 11 (2) ◽  
pp. 469-490
Author(s):  
Florian Ehmele ◽  
Lisa-Ann Kautz ◽  
Hendrik Feldmann ◽  
Joaquim G. Pinto
Keyword(s):  
Regional Climate Model ◽  
Central Europe ◽  
Climate Model ◽  
Extreme Values ◽  
Regional Climate ◽  
Horizontal Resolution ◽  
Small Scale ◽  
Data Sets ◽  
Large Ensemble

Abstract. Widespread flooding events are among the major natural hazards in central Europe. Such events are usually related to intensive, long-lasting precipitation over larger areas. Despite some prominent floods during the last three decades (e.g., 1997, 1999, 2002, and 2013), extreme floods are rare and associated with estimated long return periods of more than 100 years. To assess the associated risks of such extreme events, reliable statistics of precipitation and discharge are required. Comprehensive observations, however, are mainly available for the last 50–60 years or less. This shortcoming can be reduced using stochastic data sets. One possibility towards this aim is to consider climate model data or extended reanalyses. This study presents and discusses a validation of different century-long data sets, decadal hindcasts, and also predictions for the upcoming decade combined to a new large ensemble. Global reanalyses for the 20th century with a horizontal resolution of more than 100 km have been dynamically downscaled with a regional climate model (Consortium for Small-scale Modeling – CLimate Mode; COSMO-CLM) towards a higher resolution of 25 km. The new data sets are first filtered using a dry-day adjustment. Evaluation focuses on intensive widespread precipitation events and related temporal variabilities and trends. The presented ensemble data are within the range of observations for both statistical distributions and time series. The temporal evolution during the past 60 years is captured. The results reveal some long-term variability with phases of increased and decreased precipitation rates. The overall trend varies between the investigation areas but is mostly significant. The predictions for the upcoming decade show ongoing tendencies with increased areal precipitation. The presented regional climate model (RCM) ensemble not only allows for more robust statistics in general, it is also suitable for a better estimation of extreme values.

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