scholarly journals Influence of spatial resolution on regional climate model derived wind climates

2012 ◽  
Vol 117 (D3) ◽  
pp. n/a-n/a ◽  
Author(s):  
S. C. Pryor ◽  
G. Nikulin ◽  
C. Jones
Keyword(s):  
Regional Climate Model ◽  
Spatial Resolution ◽  
Climate Model ◽  
Regional Climate

scholarly journals Impact of spatial resolution on the modelling of the Greenland ice sheet surface mass balance between 1990–2010, using the regional climate model MAR

2012 ◽  
Vol 6 (3) ◽  
pp. 695-711 ◽  
Author(s):  
B. Franco ◽  
X. Fettweis ◽  
C. Lang ◽  
M. Erpicum
Keyword(s):  
Regional Climate Model ◽  
Mass Balance ◽  
Spatial Resolution ◽  
Climate Model ◽  
Regional Climate ◽  
Ice Sheet ◽  
Greenland Ice Sheet ◽  
Dynamical Model ◽  
Surface Mass Balance ◽  
Surface Mass

Abstract. With the aim to force an ice dynamical model, the Greenland ice sheet (GrIS) surface mass balance (SMB) was modelled at different spatial resolutions (15–50 km) for the period 1990–2010, using the regional climate model MAR (Modèle Atmosphérique Régional) forced by the ERA-INTERIM reanalysis. This comparison revealed that (i) the inter-annual variability of the SMB components is consistent within the different spatial resolutions investigated, (ii) the MAR model simulates heavier precipitation on average over the GrIS with decreasing spatial resolution, and (iii) the SMB components (except precipitation) can be derived from a simulation at lower resolution with an "intelligent" interpolation. This interpolation can also be used to approximate the SMB components over another topography/ice sheet mask of the GrIS. These results are important for the forcing of an ice dynamical model needed to enable future projections of the GrIS contribution to sea level rise over the coming centuries.

scholarly journals Dynamical Downscaling Luaran Global Climate Model (GCM) Menggunakan Model REGCM3 untuk Proyeksi Curah Hujan di Kabupaten Indramayu

Agromet ◽  
2018 ◽  
Vol 28 (1) ◽  
pp. 9
Author(s):  
Syamsu Dwi Jadmiko ◽  
Akhmad Faqih
Keyword(s):  
Regional Climate Model ◽  
Spatial Resolution ◽  
Climate Model ◽  
Dynamical Downscaling ◽  
High Spatial Resolution ◽  
Global Climate ◽  
Global Climate Model ◽  
Regional Climate ◽  
Equations Of Motion ◽  
Daily Rainfall

Future rainfall projection can be predicted by using Global Climate Model (GCM). In spite of low resolution, we are not able specifically to describe a local or regional information. Therefore, we applied downscaling technique of GCM output using Regional Climate Model (RCM). In this case, Regional Climate Model version 3 (RegCM3) is used to accomplish this purpose. RegCM3 is regional climate model which atmospheric properties are calculated by solving equations of motion and thermodynamics. Thus, RegCM3 is also called as dynamic downscaling model. RegCM3 has reliable capability to evaluate local or regional climate in high spatial resolution up to 10 × 10 km. In this study, dynamically downscaling techniques was applied to produce high spatial resolution (20 × 20 km) from GCM EH5OM output which commonly has rough spatial resolution (1.875<sup>o</sup> × 1.875<sup>o</sup>). Simulation show that future rainfall in Indramayu is relatively decreased compared to the baseline condition. Decreased rainfall generally occurs during the dry season (July-June-August/JJA) in a range 10-20%. Study of extreme daily rainfall indicates that there is no significant increase or decrease value.

Sensitivity of seasonal flood simulations to regional climate model spatial resolution

2019 ◽  
Vol 53 (7-8) ◽  
pp. 4337-4354
Author(s):  
Mariana Castaneda-Gonzalez ◽  
Annie Poulin ◽  
Rabindranarth Romero-Lopez ◽  
Richard Arsenault ◽  
François Brissette ◽  
...  
Keyword(s):  
Regional Climate Model ◽  
Spatial Resolution ◽  
Climate Model ◽  
Regional Climate ◽  
Seasonal Flood ◽  
Flood Simulations

Does increasing the spatial resolution of a regional climate model improve the simulated daily precipitation?

2012 ◽  
Vol 41 (5-6) ◽  
pp. 1475-1495 ◽  
Author(s):  
Steven C. Chan ◽  
Elizabeth J. Kendon ◽  
Hayley J. Fowler ◽  
Stephen Blenkinsop ◽  
Christopher A. T. Ferro ◽  
...  
Keyword(s):  
Regional Climate Model ◽  
Spatial Resolution ◽  
Climate Model ◽  
Daily Precipitation ◽  
Regional Climate

scholarly journals Melting over the East Antarctic Peninsula (1999–2009): evaluation of a high-resolution regional climate model

2017 ◽  
Author(s):  
Rajashree T. Datta ◽  
Marco Tedesco ◽  
Cecile Agosta ◽  
Xavier Fettweis ◽  
Peter Kuipers Munneke ◽  
...  
Keyword(s):  
High Resolution ◽  
Regional Climate Model ◽  
Spatial Resolution ◽  
Antarctic Peninsula ◽  
Climate Model ◽  
Regional Climate ◽  
Ice Shelf ◽  
Circulation Patterns ◽  
The Antarctic ◽  
Microwave Data

Abstract. Surface melting over the Antarctic Peninsula (AP) plays a crucial role for the stability of ice shelves and dynamics of grounded ice, hence modulating the mass balance in a region of the world which is particularly sensitive to increasing surface temperatures. Understanding the processes that drive melting using surface energy and mass balance models is fundamental to improving estimates of current and future surface melting and associated sea level rise through ice-shelf collapse. This is even more important in view of both the paucity of in-situ measurements in Antarctica generally and the specific challenges presented by the circulation patterns over the Antarctic Peninsula. In this study, we evaluate the regional climate model Modèle Atmosphérique Régionale (MAR) over the Antarctic Peninsula (AP) at a 10 km spatial resolution between 1999 and 2009, a period which coincides with the availability of active microwave data from the QuikSCAT mission. This is the first time that this model, which has been validated extensively over Greenland, has been applied to the Antarctic Peninsula at a high resolution. We compare melt occurrence modeled by MAR with a combination of estimates from passive and active microwave data. Our primary regional focus is the northern East Antarctic Peninsula (East AP), where we evaluate MAR against wind and temperature data collected by three automatic weather stations (AWS). Our results indicate that satellites estimates show greater melt frequency, a larger melt extent, and a quicker expansion to peak melt extent than MAR in the center and east of the Larsen C ice shelf. The difference between the remote sensing and modeled estimates reduces in the north and west of the East AP. Melting in the East AP can be initiated by both sporadic westerly föhn flow over the AP and northerly winds advecting warm air from lower latitudes. To quantify MAR's ability to simulate different circulation patterns that affect melt, we take a unique approach to evaluate melt occurrence (using satellite data) and concurrent temperature biases associated with specific wind direction biases using AWS data over the Larsen Ice Shelf. Our results indicate that although MAR shows an overall warm bias, it also shows fewer warm, strong westerly winds than reported by AWS stations, which may lead to an underestimation of melt. The underestimation of föhn flow in the east of the Larsen C may potentially be resolved by removing the hydrostatic assumption in MAR or increasing spatial resolution. The underestimation of southwesterly flow in particular may be reduced by using higher-resolution topography.

scholarly journals Impact of spatial resolution on the modelling of the Greenland ice sheet surface mass balance between 1990–2010, using the regional climate model MAR

2012 ◽  
Vol 6 (1) ◽  
pp. 635-672 ◽  
Author(s):  
B. Franco ◽  
X. Fettweis ◽  
C. Lang ◽  
M. Erpicum
Keyword(s):  
Regional Climate Model ◽  
Mass Balance ◽  
Spatial Resolution ◽  
Climate Model ◽  
Regional Climate ◽  
Ice Sheet ◽  
Greenland Ice Sheet ◽  
Dynamical Model ◽  
Surface Mass Balance ◽  
Surface Mass

Abstract. With the aim to force an ice dynamical model, the Greenland ice sheet (GrIS) surface mass balance (SMB) was modelled at different spatial resolutions (15–50 km) for the period 1990–2010, using the regional climate model MAR (Modèle Atmosphérique Régional) forced by the ERA-INTERIM reanalysis. This comparison revealed that (i) the inter-annual variability of the SMB components is consistent within the different spatial resolutions investigated, (ii) the MAR model simulates heavier precipitation on average over the GrIS with diminishing spatial resolution, and (iii) the SMB components (except precipitation) can be derived from a simulation at lower resolution with an ''intelligent'' interpolation. This interpolation can also be used to approximate the SMB components over another topography/ice sheet mask of the GrIS. These results are important for the forcing of an ice dynamical model, needed to enable future projections of the GrIS contribution to sea level rise over the coming centuries.

Regional paleoclimate in the EMME and the Nile basin based on COSMO-CLM with orbital and volcanic forcing at the different spatial resolution

2021 ◽  
Author(s):  
Mingyue Zhang ◽  
Eva Hartmann ◽  
Elena Xoplaki ◽  
Sebastian Wagner
Keyword(s):  
Climate Variability ◽  
Regional Climate Model ◽  
Observational Data ◽  
River Basin ◽  
Spatial Resolution ◽  
Climate Model ◽  
Regional Climate ◽  
Nile River ◽  
Model Simulations ◽  
Volcanic Forcing

&lt;p&gt;The interaction between climate variability, extreme events and societies in the Eastern Mediterranean and the Middle East (EMME) and the Nile river basin is of particular interest in the last 2000 years. Major civilizations and complex pre-modern societies have written the greatest and multifaceted history of the area. However, the influence of climate on the societies is examined only from the proxy records perspective, without the detail of the processes that offer regional climate model simulations. The present and future climate and climate variability of this region are currently studied in the frame of the MENA CORDEX program with different global and regional climate models. For the past climate, exist only global climate or earth system model simulations with a coarse spatial resolution with a minimum of 100 km horizontal resolution. We aim at improving our understanding of past climate in the EMME and the Nile river basin (Nile) at the regional scale and use an adjusted paleoclimate version of the COSMO-CLM. Test simulations have been performed over the study region for the years 2017-2018 to identify the best settings of CCLM with respect to the CORDEX-MENA simulations which are carried out by Bucchignani et al. (2016). Test simulations show the CCLM can correctly simulate large tropical volcanic eruptions, as conditions similar to the Tambora eruption by adapting the stratospheric aerosol optical depth (AOD) mimicking conditions after a Tambora-like volcanic eruption. In agreement with Bucchignani et al. (2016), the albedo and aerosols parameters are found to be most important for the area and may be responsible for larger deviations compared to observational data.&amp;#160; Thus, CCLM climate modelling for the present (1979-2019) and selected paleo-periods (525-575 CE and 1220-1290 CE) with intense volcanic activity will be forced by the MPI-ESM-LR &amp;#8216;past2k&amp;#8217; simulation with the optimized settings which is identified in the test simulations. Orbital, solar and volcanic forcing, together with vegetation, land-use changes and greenhouse gas changes will be addressed step by step in the CCLM with resolutions of 0.44&amp;#176; and 0.11&amp;#176;. &amp;#160;The present-day simulations show that the temperature and precipitation are well simulated compare to reanalysis and observational data in general. Additional, CCLM correctly captured convection and cloud cover clearly define the model performance in the greater southern areas of the domain that are affected by the tropical convection. Further, the orography and the land-sea interaction seem to significantly influence the local climate and may lead to differences compared to observations, which may also be strongly connected with the specific spatial resolution. For example, the Ethiopian Highlands and the East African Plateau have high elevations and have a large impact on the regional climate.&lt;/p&gt;&lt;p&gt;&amp;#160;&lt;strong&gt;Reference&lt;/strong&gt;&lt;/p&gt;&lt;p&gt;Bucchignani, E., Cattaneo, L., Panitz, HJ.&amp;#160;et al.&amp;#160;Sensitivity analysis with the regional climate model COSMO-CLM over the CORDEX-MENA domain.&amp;#160;Meteorol Atmos Phys&amp;#160;&lt;strong&gt;128,&amp;#160;&lt;/strong&gt;73&amp;#8211;95 (2016). https://doi.org/10.1007/s00703-015-0403-3&lt;/p&gt;

GCM Model Selection Procedure for Downscaling

2021 ◽  
Author(s):  
Carolyne Pickler ◽  
Thomas Mölg
Keyword(s):  
Surface Temperature ◽  
Regional Climate Model ◽  
East Africa ◽  
Spatial Resolution ◽  
General Circulation ◽  
Climate Model ◽  
Regional Climate ◽  
Selection Procedure ◽  
Reanalysis Data ◽  
Local Climate

&lt;p&gt;Downscaling has been widely used in studies of regional and/or local climate as it yields greater spatial resolution than general circulation models (GCM) can provide. &amp;#160;It can approached in two distinct ways: 1) Statistical and 2) Dynamical. &amp;#160;Statistical downscaling utilizes mathematical relationships between large-scale and regional/local climate to transform GCM or reanalysis data to a higher spatial resolution. &amp;#160;Dynamical downscaling comprises forcing the lateral boundaries of a regional climate model with reanalysis or GCM data. &amp;#160;However, there is no set technique to select said GCM(s).&lt;/p&gt;&lt;p&gt;A comprehensive yet easily applicable selection procedure was created to address this. &amp;#160;Using reanalysis data and/or observational data, the space-time climatic anomalies and the mean state of the climate are evaluated for the region of interest. &amp;#160;East Africa was utilized as a case study and GISS-E2-H r6i1p3 was found to perform the strongest. &amp;#160;This procedure cannot, however, tell whether the models can reproduce the key processes of the region. &amp;#160;To examine this, the ability of the models to simulate the Indian Ocean Dipole were evaluated. &amp;#160;It was found that higher ranked models were better able to capture it than lower ranked ones. &amp;#160;Furthermore, to ensure that a higher ranked model yielded a better downscaling simulation, three 10-year regional climate model simulations over East Africa were undertaken, where they were respectively forced by the highest ranked GCM (GISS-E2-H r6i1p3), the lowest ranked GCM (IPSL-CM5A-LR r4i1p1) and the MERRA-2 reanalysis product. &amp;#160;The simulated surface temperature and precipitation for Equatorial East Africa were compared with a gridded observational dataset (CRU TS 4.04). &amp;#160;Results showed that the higher ranked GCM produced a better downscaled simulation than the lower ranked one, a result that was more evident for surface temperature than precipitation.&lt;/p&gt;

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