REMOLAND: New high-resolution surface boundary data for the regional climate model REMO and their impacts

2020 ◽  
Author(s):  
Katrin Ziegler ◽  
Felix Pollinger ◽  
Daniel Abel ◽  
Heiko Paeth
Keyword(s):  
High Resolution ◽  
Regional Climate Model ◽  
Land Surface ◽  
Climate Models ◽  
Climate Model ◽  
Regional Climate ◽  
Regional Climate Models ◽  
Data Sets ◽  
Surface Boundary ◽  
Local Climate

<p class="western" align="justify"><span lang="en-US">In cooperation with the Climate Service Center Germany (GERICS) we want to improve the land surface module in the regional climate model REMO. Due to the need of high-resolution regional climate models to get information about local climate change, new data and new processes have to be integrated in these models.</span></p> <p class="western" align="justify"><span lang="en-US">Based on the REMO2015 version and focusing on EUR-CORDEX region we included and compared five different high-resolution topographic data sets. To improve the thermal and hydrological processes in the model’s soil we also tested three new soil data sets with a much higher spatial resolution and with new parameters for a new soil parameterization.</span></p>

Future response of precipitation extremes over the Nordic region in a convection-permitting regional climate model

2020 ◽  
Author(s):  
Petter Lind ◽  
Danijel Belušić ◽  
Erik Kjellström ◽  
Fuxing Wang ◽  
Erika Toivonen ◽  
...  
Keyword(s):  
High Resolution ◽  
Regional Climate Model ◽  
Boundary Data ◽  
Climate Models ◽  
Climate Model ◽  
Regional Climate ◽  
Precipitation Extremes ◽  
Grid Spacing ◽  
Local Climate ◽  
Nordic Region

<p>There is an increased need for more detailed climate information from impact researchers, stakeholders and policy makers for regional-to-local climate change assessments. In order to design relevant and informative planning strategies on these scales it is important to have reliable climate data and information on high spatial O(1km) and temporal (daily to sub-daily) scales. Such high-resolution data is also beneficial for climate impact modellers as input to their models, e.g. hydrological or urban models that operate on regional to local scales. It has been established that regional climate models (RCMs) provide added value compared to coarser global climate models (GCMs) or re-analysis (e.g. ERA-Interim). However, RCMs with standard spatial resolution O(10 − 50km) still suffer from inadequacies in representing important regional-to-local climate phenomena and characteristics, both from the implied ”smoothening” effect within each grid cell which limits the representation of fine scale surface forcings, and the need to parameterize small-scale processes like atmospheric convection. The latter particularly invokes uncertainties in future climate responses of short-duration precipitation extremes such as flash-floods. Here, we compare 20-year simulations with a very high resolution (3 km grid spacing) convection permitting regional climate model (CPRCM) with a standard high-resolution (12 km grid spacing) convection parameterized RCM and their abilities to simulate the climate characteristics of the Nordic region in Europe, with particular focus on precipitation extremes. The study covers both recent past (with boundary data from ERA-Interim and the EC-Earth GCM) and the end of the 21st century (boundary data from EC-Earth using the RCP8.5 radiative forcing scenario). The high model grid resolution combined with the extensive simulated time period which enables assessment on climatological time scales makes this study one of very few for this region.</p>

scholarly journals High-Resolution Simulations of Wintertime Precipitation in the Colorado Headwaters Region: Sensitivity to Physics Parameterizations

2011 ◽  
Vol 139 (11) ◽  
pp. 3533-3553 ◽  
Author(s):  
Changhai Liu ◽  
Kyoko Ikeda ◽  
Gregory Thompson ◽  
Roy Rasmussen ◽  
Jimy Dudhia
Keyword(s):  
High Resolution ◽  
Land Surface ◽  
Climate Models ◽  
Climate Model ◽  
Regional Climate ◽  
Regional Climate Models ◽  
Cold Season ◽  
Cloud Microphysics ◽  
Physics Parameterizations ◽  
Heating Profiles

Abstract An investigation was conducted on the effects of various physics parameterizations on wintertime precipitation predictions using a high-resolution regional climate model. The objective was to evaluate the sensitivity of cold-season mountainous snowfall to cloud microphysics schemes, planetary boundary layer (PBL) schemes, land surface schemes, and radiative transfer schemes at a 4-km grid spacing applicable to the next generation of regional climate models. The results indicated that orographically enhanced precipitation was highly sensitive to cloud microphysics parameterizations. Of the tested 7 parameterizations, 2 schemes clearly outperformed the others that overpredicted the snowfall amount by as much as ~30%–60% on the basis of snow telemetry observations. Significant differences among these schemes were apparent in domain averages, spatial distributions of hydrometeors, latent heating profiles, and cloud fields. In comparison, model results showed relatively weak dependency on the land surface, PBL, and radiation schemes, roughly in the order of decreasing level of sensitivity.

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>

Impact of different and time-varying land cover data sets in a regional climate model on regional and local climate over Europe

2020 ◽  
Author(s):  
Mingyue Zhang ◽  
Jürgen Helmert ◽  
Merja Tölle
Keyword(s):  
Land Cover ◽  
Regional Climate Model ◽  
Climate Model ◽  
Regional Climate ◽  
Data Sets ◽  
Time Varying ◽  
Data Set ◽  
Local Climate ◽  
Land Cover Data ◽  
Simulation Results

<p>According to IPCC, Land use and Land Cover (LC) changes have a key role to adapt and mitigate future climate change aiming to stabilize temperature rise up to 2°C. Land surface change at regional scale is associated to global climate change, such as global warming. It influences the earth’s water and energy cycles via influences on the heat, moisture and momentum transfer, and on the chemical composition of the atmosphere. These effects show variations due to different LC types, and due to their spatial and temporal resolutions.  Thus, we incorporate a new time-varying land cover data set based on ESACCI into the regional climate model COSMO-CLM(v5.0). Further, the impact on the regional and local climate is compared to the standard operational LC data of GLC2000 and GlobCover 2009. Convection-permitting simulations with the three land cover data sets are performed at 0.0275° horizontal resolution over Europe for the time period from 1992 to 2015.</p><p>Overall, the simulation results show comparable agreement to observations. However, the simulation results based on GLC2000 and GlobCover 2009 (with 23 LC types) LC data sets show a fluctuation of 0.5K in temperature and 5% of precipitation. Even though the LC is classified into the same types, the difference in LC distribution and fraction leads to variations in climate simulation results. Using all of the 37 LC types of the ESACCI-LC data set show noticeable differences in distribution of temperature and precipitation compared to the simulations with GLC2000 and GlobCover 2009. Especially in forest areas, slight differences of the plant cover type (e.g. Evergreen or Deciduous) could result in up to 10% differences (increase or decrease) in temperature and precipitation over the simulation domain. Our results demonstrate how LC changes as well as different land cover type effect regional climate. There is need for proper and time-varying land cover data sets for regional climate model studies. The approach of including ESACCI-LC data set into regional climate model simulations also improved the external data generation system.</p><p>We anticipate this research to be a starting point for involving time-varying LC data sets into regional climate models. Furthermore, it will give us a possibility to quantify the effect of time-varying LC data on regional climate accurately.</p><p><strong>Acknowledgement</strong>:</p><p>1: Computational resources were made available by the German Climate Computing Center (DKRZ) through support from the Federal Ministry of Education and Research in Germany (BMBF). We acknowledge the funding of the German Research Foundation (DFG) through grant NR. 401857120.</p><p>2: Appreciation for the support of Jürg Luterbacher and Eva Nowatzki.</p><p> </p>

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.

Evaluation of the regional climate model RegCM4.7 over the Carpathian region for very wet and average years

2020 ◽  
Author(s):  
Tímea Kalmár ◽  
Ildikó Pieczka ◽  
Rita Pongrácz
Keyword(s):  
Soil Moisture ◽  
Regional Climate Model ◽  
Land Surface ◽  
Climate Models ◽  
Climate Model ◽  
Regional Climate ◽  
Cloud Microphysics ◽  
Mountainous Area ◽  
Model Version ◽  
Carpathian Region

<p>Precipitation is one of the most important climate variables in many aspects due to its key impact on agriculture, water management, etc. However, it remains a challenge for climate models to realistically simulate the regional patterns, temporal variations, and intensity of precipitation. The difficulty arises from the complexity of precipitation processes within the atmosphere stemming from cloud microphysics, cumulus convection, large-scale circulations, planetary boundary layer (PBL) processes, and many others. This is especially true for heterogeneous surfaces with complex orography such as the Carpathian region.  Thus, the Carpathian Basin, with its surrounding mountains, requires higher model resolution, along with different parameterizations, compared to more homogenous regions. The aim of the study is to reproduce the historical precipitation pattern through testing the parameterization of surface processes. The appropriate representations of land surface component in climate models are essential for the simulation of surface and subsurface runoff, soil moisture, and evapotranspiration. Furthermore, PBL strongly influences temperature, moisture, and wind through the turbulent transfer of air mass. The current study focuses on the newest model version of RegCM (RegCM4.7), with which we carry out simulations using different parameterization schemes over the Carpathian region. We investigate the effects of land-surface schemes (i.e. BATS - Biosphere-Atmosphere Transfer Scheme and CLM4.5 - Community Land Model version 4.5) in the regional climate model. Studies over different regions have shown that CLM offers improvements in terms of land–atmosphere exchanges of moisture and energy and associated surface climate feedbacks compared with BATS. Our aim includes evaluating whether this is the case for the Carpathian region.</p><p>Four 1-year-long experiments both for 1981 and 2010 (excluding the spin-up time) are completed using the same domain, initial and lateral atmospheric boundary data conditions (i.e. ERA-Interim), with a 10 km spatial resolution. These years were chosen because 1981 was a normal year in terms of precipitation, while 2010 was the wettest year in Hungary from the beginning of the 20th century. We carry out a detailed analysis of RegCM outputs focusing not only on standard climatological variables (precipitation and temperature), but also on additional meteorological variables, which have important roles in the water cycle (e.g. soil moisture, evapotranspiration). The simulations are compared with the CARPATCLIM observed, homogenised, gridded dataset and other databases (ESA CCI Soil Moisture Product New Version Release (v04.5) and Surface Solar Radiation Data Set - Heliosat (SARAH)). It is found that the simulated near-surface temperature and precipitation are better represented in the CLM scheme than in the BATS when compared with observations, both over the lowland and mountainous area. The model simulations also show that the precipitation is overestimated more over mountainous area in 2010 than in 1981.  </p>

Intercomparison of Arctic Regional Climate Models: Modeling Clouds and Radiation for SHEBA in May 1998

2006 ◽  
Vol 19 (17) ◽  
pp. 4167-4178 ◽  
Author(s):  
Jun Inoue ◽  
Jiping Liu ◽  
James O. Pinto ◽  
Judith A. Curry
Keyword(s):  
Radiative Transfer ◽  
Climate Models ◽  
Climate Model ◽  
Regional Climate ◽  
Regional Climate Models ◽  
Cloud Microphysics ◽  
Surface Heat ◽  
The Arctic ◽  
Surface Boundary ◽  
Simulated Surface

Abstract To improve simulations of the Arctic climate and to quantify climate model errors, four regional climate models [the Arctic Regional Climate System Model (ARCSYM), the Coupled Ocean–Atmosphere Mesoscale Prediction System (COAMPS), the High-Resolution Limited-Area Model (HIRHAM), and the Rossby Center Atmospheric Model (RCA)] have simulated the annual Surface Heat Budget of the Arctic Ocean (SHEBA) under the Arctic Regional Climate Model Intercomparison Project (ARCMIP). The same lateral boundary and ocean surface boundary conditions (i.e., ice concentration and surface temperature) drive all of the models. This study evaluated modeled surface heat fluxes and cloud fields during May 1998, a month that included the onset of the surface icemelt. In general, observations agreed with simulated surface pressure and near-surface air properties. Simulation errors due to surface fluxes and cloud effects biased the net simulated surface heat flux, which in turn affected the timing of the simulated icemelt. Modeled cloud geometry and precipitation suggest that the RCA model produced the most accurate cloud scheme, followed by the HIRHAM model. Evaluation of a relationship between cloud water paths and radiation showed that a radiative transfer scheme in ARCSYM was closely matched with the observation when liquid clouds were dominant. Clouds and radiation are of course closely linked, and an additional comparison of the radiative transfer codes for ARCSYM and COAMPS was performed for clear-sky conditions, thereby excluding cloud effects. Overall, the schemes for radiative transfer in ARCSYM and for cloud microphysics in RCA potentially have some advantages for modeling the springtime Arctic.

scholarly journals Uncertainties in climate change projections and regional downscaling: implications for water resources management

2010 ◽  
Vol 7 (2) ◽  
pp. 1821-1848 ◽  
Author(s):  
W. Buytaert ◽  
M. Vuille ◽  
A. Dewulf ◽  
R. Urrutia ◽  
A. Karmalkar ◽  
...  
Keyword(s):  
Climate Change ◽  
Water Resources ◽  
Regional Climate Model ◽  
Water Resources Management ◽  
Climate Models ◽  
Climate Model ◽  
Regional Climate ◽  
Regional Climate Models ◽  
Climate Change Projections ◽  
Resources Management

Abstract. Climate change is expected to have a large impact on water resources worldwide. A major problem in assessing the potential impact of a changing climate on these resources is the difference in spatial scale between available climate change projections and water resources management. Regional climate models (RCMs) are often used for the spatial disaggregation of the outputs of global circulation models. However, RCMs are time-intensive to run and typically only a small number of model runs is available for a certain region of interest. This paper investigates the value of the improved representation of local climate processes by a regional climate model for water resources management in the tropical Andes of Ecuador. This region has a complex hydrology and its water resources are under pressure. Compared to the IPCC AR4 model ensemble, the regional climate model PRECIS does indeed capture local gradients better than global models, but locally the model is prone to large discrepancies between observed and modelled precipitation. It is concluded that a further increase in resolution is necessary to represent local gradients properly. Furthermore, to assess the uncertainty in downscaling, an ensemble of regional climate models should be implemented. Finally, translating the climate variables to streamflow using a hydrological model constitutes a smaller but not negligible source of uncertainty.

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