scholarly journals Assessment of Dynamical Downscaling in Near-Surface Fields with Different Spectral Nudging Approaches Using the Nested Regional Climate Model (NRCM)

2013 ◽  
Vol 52 (7) ◽  
pp. 1576-1591 ◽  
Author(s):  
Jiali Wang ◽  
Veerabhadra R. Kotamarthi
Keyword(s):  
Surface Temperature ◽  
Regional Climate Model ◽  
Climate Model ◽  
Regional Climate ◽  
Regional Scale ◽  
The United States ◽  
Near Surface ◽  
Spectral Nudging ◽  
Short Period ◽  
Surface Fields

AbstractDynamic downscaling with regional-scale climate models is used widely for increasing the spatial resolution of global-scale climate model projections. One uncertainty in generating these projections is the choice of boundary forcing applied. In this study the Nested Regional Climate Model (NRCM) is used with a grid spacing of 12 km over the United States (excluding Hawaii) to dynamically downscale 2.5° National Centers for Environmental Prediction–U.S. Department of Energy Reanalysis-2 data, with different applications of spectral nudging (SN) for the boundary conditions. Nine numerical experiments for July 2005—each with different wavenumbers and nudging duration periods, applied to different model layers—evaluated the performance of SN in downscaling near-surface fields. The calculations were compared with the North America Regional Reanalysis dataset over four subregions of the contiguous 48 states. Results show significant differences with different wavenumbers, nudging duration periods, and nudging altitudes. The short-period SN with three waves, applied above 850 hPa, showed the highest skill in simulating precipitation, whereas whole-period SN produced a higher skill level and performed slightly better than short-period SN for surface temperature and 10-m wind, respectively. Differences in the performance of SN applied at different altitudes were not significant. On the basis of the comparisons for precipitation, surface temperature, and wind fields over entire contiguous states, whole-period nudging with six waves starting above 850 hPa for downscaling calculations for climate-related variables is recommended. This method improved the performance of the NRCM in predicting near-surface fields by more than 30.5% relative to a case with no nudging.

scholarly journals Drifting snow climate of the Greenland ice sheet: a study with a regional climate model

2012 ◽  
Vol 6 (3) ◽  
pp. 1611-1635 ◽  
Author(s):  
J. T. M. Lenaerts ◽  
M. R. van den Broeke ◽  
J. H. van Angelen ◽  
E. van Meijgaard ◽  
S. J. Déry
Keyword(s):  
Wind Speed ◽  
Regional Climate Model ◽  
Climate Model ◽  
Regional Climate ◽  
Regional Scale ◽  
Winter Season ◽  
Ice Sheet ◽  
Greenland Ice Sheet ◽  
Near Surface ◽  
Drifting Snow

Abstract. This paper presents the drifting snow climate of the Greenland ice sheet, using output from a high-resolution (~11 km) regional climate model (RACMO2). Because reliable direct observations of drifting snow do not exist, we evaluate the modeled near-surface climate instead, using Automatic Weather Station (AWS) observations from the K-transect and find that RACMO2 realistically simulates near-surface wind speed and relative humidity, two variables that are important for drifting snow. Integrated over the ice sheet, drifting snow sublimation (SUds) equals 24 ± 3 Gt yr−1, and is significantly larger than surface sublimation (SUs, 16 ± 2 Gt yr−1). SUds strongly varies between seasons, and is only important in winter, when surface sublimation and runoff are small. A rapid transition exists between the winter season, when snowfall and SUds are important, and the summer season, when snowmelt is significant, which increases surface snow density and thereby limits drifting snow processes. Drifting snow erosion (ERds) is only important on a regional scale. In recent decades, following decreasing wind speed and rising near-surface temperatures, SUds exhibits a negative trend (0.1 ± 0.1 Gt yr−1), which is compensated by an increase in SUs of similar magnitude.

scholarly journals Drifting snow climate of the Greenland ice sheet: a study with a regional climate model

2012 ◽  
Vol 6 (4) ◽  
pp. 891-899 ◽  
Author(s):  
J. T. M. Lenaerts ◽  
M. R. van den Broeke ◽  
J. H. van Angelen ◽  
E. van Meijgaard ◽  
S. J. Déry
Keyword(s):  
Wind Speed ◽  
Regional Climate Model ◽  
Climate Model ◽  
Regional Climate ◽  
Regional Scale ◽  
Winter Season ◽  
Ice Sheet ◽  
Greenland Ice Sheet ◽  
Near Surface ◽  
Drifting Snow

Abstract. This paper presents the drifting snow climate of the Greenland ice sheet, using output from a high-resolution (∼11 km) regional climate model. Because reliable direct observations of drifting snow do not exist, we evaluate the modeled near-surface climate instead, using automatic weather station (AWS) observations from the K-transect and find that RACMO2 realistically simulates near-surface wind speed and relative humidity, two variables that are important for drifting snow. Integrated over the ice sheet, drifting snow sublimation (SUds) equals 24 ± 3 Gt yr−1, and is significantly larger than surface sublimation (SUs, 16 ± 2 Gt yr−1). SUds strongly varies between seasons, and is only important in winter, when surface sublimation and runoff are small. A rapid transition exists between the winter season, when snowfall and SUds are important, and the summer season, when snowmelt is significant, which increases surface snow density and thereby limits drifting snow processes. Drifting snow erosion (ERds) is only important on a regional scale. In recent decades, following decreasing wind speed and rising near-surface temperatures, SUds exhibits a negative trend (0.1 ± 0.1 Gt yr−1), which is compensated by an increase in SUs of similar magnitude.

scholarly journals Recent Near-surface Temperature Trends in the Antarctic Peninsula from Observed, Reanalysis and Regional Climate Model Data

2020 ◽  
Vol 37 (5) ◽  
pp. 477-493
Author(s):  
Deniz Bozkurt ◽  
David H. Bromwich ◽  
Jorge Carrasco ◽  
Keith M. Hines ◽  
Juan Carlos Maureira ◽  
...  
Keyword(s):  
Surface Temperature ◽  
Regional Climate Model ◽  
Antarctic Peninsula ◽  
Climate Model ◽  
Regional Climate ◽  
Model Data ◽  
Near Surface ◽  
Temperature Trends ◽  
The Antarctic

Regional Climate Model Inter-Comparison for Antarctica within a Data Science Framework

2021 ◽  
Author(s):  
Jeremy Carter ◽  
Amber Leeson ◽  
Andrew Orr ◽  
Christoph Kittel ◽  
Melchior van Wessem
Keyword(s):  
Regional Climate Model ◽  
Climate Models ◽  
Climate Model ◽  
Regional Climate ◽  
Regional Climate Models ◽  
Future Climate Change ◽  
Near Surface ◽  
Virtual Lab ◽  
Model Variability ◽  
The Antarctic

<p>Understanding the surface climatology of the Antarctic ice sheet is essential if we are to adequately predict its response to future climate change. This includes both primary impacts such as increased ice melting and secondary impacts such as ice shelf collapse events. Given its size, and inhospitable environment, weather stations on Antarctica are sparse. Thus, we rely on regional climate models to 1) develop our understanding of how the climate of Antarctica varies in both time and space and 2) provide data to use as context for remote sensing studies and forcing for dynamical process models. Given that there are a number of different regional climate models available that explicitly simulate Antarctic climate, understanding inter- and intra model variability is important.</p><p>Here, inter- and intra-model variability in Antarctic-wide regional climate model output is assessed for: snowfall; rainfall; snowmelt and near-surface air temperature within a cloud-based virtual lab framework. State-of-the-art regional climate model runs from the Antarctic-CORDEX project using the RACMO, MAR and MetUM models are used, together with the ERA5 and ERA-Interim reanalyses products. Multiple simulations using the same model and domain boundary but run at either different spatial resolutions or with different driving data are used. Traditional analysis techniques are exploited and the question of potential added value from more modern and involved methods such as the use of Gaussian Processes is investigated. The advantages of using a virtual lab in a cloud based environment for increasing transparency and reproducibility, are demonstrated, with a view to ultimately make the code and methods used widely available for other research groups.</p>

Comparison of different infiltration schemes in the regional climate model REMO

2021 ◽  
Author(s):  
Daniel Abel ◽  
Katrin Ziegler ◽  
Felix Pollinger ◽  
Heiko Paeth
Keyword(s):  
Regional Climate Model ◽  
Climate Models ◽  
Climate Model ◽  
Root Zone ◽  
Regional Climate ◽  
Climate Projections ◽  
Soil Hydrology ◽  
Spatiotemporal Resolution ◽  
Development Fund ◽  
Near Surface

<p>The European Regional Development Fund-Project BigData@Geo aims to create highly resolved climate projections for the model region of Lower Franconia in Bavaria, Germany. These projections are analyzed and made available to local stakeholders of agriculture, forestry, and viniculture as well as general public. Since regional climate models’ spatiotemporal resolution often is too coarse to deal with such local issues, the regional climate model REMO is improved within the frame of the project in cooperation with the Climate Service Center Germany (GERICS).</p><p>Accurate and highly resolved climate projections require realistic modeling of soil hydrology. Thus, REMO’s original bucket scheme is replaced by a 5-layer soil scheme. It allows for the representation of water below the root zone. Evaporation is possible solely from the top layer instead of the entire bucket and water can flow vertically between the layers. Consequently, the properties and processes change significantly compared to the bucket scheme. Both, the bucket and the 5-layer scheme, use the improved Arno scheme to separate throughfall into infiltration and surface runoff.</p><p>In this study, we examine if this scheme is suitable for use with the improved soil hydrology or if other schemes lead to better results. For this, we (1) modify the improved Arno scheme and further introduce the infiltration equations of (2) Philip as well as (3) Green and Ampt. First results of the comparison of these four different schemes and their influence on soil moisture and near-surface atmospheric variables are presented.</p>

scholarly journals Regional Climate Model Evaluation System powered by Apache Open Climate Workbench v1.3.0: an enabling tool for facilitating regional climate studies

2018 ◽  
Author(s):  
Huikyo Lee ◽  
Alexander Goodman ◽  
Lewis McGibbney ◽  
Duane Waliser ◽  
Jinwon Kim ◽  
...  
Keyword(s):  
Regional Climate Model ◽  
Model Evaluation ◽  
Evaluation System ◽  
Climate Models ◽  
Performance Metrics ◽  
Climate Model ◽  
Regional Climate ◽  
The United States ◽  
Climate Data ◽  
Climate Model Evaluation

Abstract. The Regional Climate Model Evaluation System (RCMES) is an enabling tool of the National Aeronautics and Space Administration to support the United States National Climate Assessment. As a comprehensive system for evaluating climate models on regional and continental scales using observational datasets from a variety of sources, RCMES is designed to yield information on the performance of climate models and guide their improvement. Here we present a user-oriented document describing the latest version of RCMES, its development process and future plans for improvements. The main objective of RCMES is to facilitate the climate model evaluation process at regional scales. RCMES provides a framework for performing systematic evaluations of climate simulations, such as those from the Coordinated Regional Climate Downscaling Experiment (CORDEX), using in-situ observations as well as satellite and reanalysis data products. The main components of RCMES are: 1) a database of observations widely used for climate model evaluation, 2) various data loaders to import climate models and observations in different formats, 3) a versatile processor to subset and regrid the loaded datasets, 4) performance metrics designed to assess and quantify model skill, 5) plotting routines to visualize the performance metrics, 6) a toolkit for statistically downscaling climate model simulations, and 7) two installation packages to maximize convenience of users without Python skills. RCMES website is maintained up to date with brief explanation of these components. Although there are other open-source software (OSS) toolkits that facilitate analysis and evaluation of climate models, there is a need for climate scientists to participate in the development and customization of OSS to study regional climate change. To establish infrastructure and to ensure software sustainability, development of RCMES is an open, publicly accessible process enabled by leveraging the Apache Software Foundation's OSS library, Apache Open Climate Workbench (OCW). The OCW software that powers RCMES includes a Python OSS library for common climate model evaluation tasks as well as a set of user-friendly interfaces for quickly configuring a model evaluation task. OCW also allows users to build their own climate data analysis tools, such as the statistical downscaling toolkit provided as a part of RCMES.

scholarly journals Study of Monsoonal Features Using Regional Climate Model over Heterogeneous Monsoon Dominated Region

2019 ◽  
Vol 101 ◽  
pp. 03004
Author(s):  
Rohit Srivastava ◽  
Ruchita Shah
Keyword(s):  
High Resolution ◽  
Regional Climate Model ◽  
Climate Model ◽  
Regional Climate ◽  
Regional Scale ◽  
Actual Performance ◽  
The Earth ◽  
Wide Range ◽  
Resolution Model ◽  
High Resolution Model

Global warming is an increase in average global temperature of the earth which lead to climate change. Heterogeneity in the earth-atmosphere system becomes difficult to capture at low resolution (1°x1°) by satellite. Such features may be captured by using high resolution model such as regional climate model (0.5°x 0.5°). This type of study is quite important for a monsoon dominated country like India where Indo-Gangetic Plains (IGP) faces highest heterogeneity due to its geographic location. Present study compares high resolution model features with satellite data over IGP for monsoon season during a normal rainfall year 2010 to understand the actual performance of model. Almost whole IGP simulates relative humidity (RH) with wide range (~50-100%), whereas satellite shows it with narrow range (~60-80%) during September, 2010. Thus model is able to pick the features which were missed by satellite. Hence further model simulation extends over India and adjoining oceanic regions which simulates data of southwest monsoon with high (~70-100%) RH, high (~0.4-0.7) cloud fraction (CF) and low (~80-200 W/m2) outgoing longwave radiation (OLR) over Arabian Sea during June, 2010. Such type of study can be useful to understand heterogeneity at regional scale with the help of high resolution model generated data.

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