scholarly journals Validation of the HIRHAM-Simulated Indian Summer Monsoon Circulation

2010 ◽  
Vol 2010 ◽  
pp. 1-14 ◽  
Author(s):  
Stefan Polanski ◽  
Annette Rinke ◽  
Klaus Dethloff
Keyword(s):  
Summer Monsoon ◽  
Climate Model ◽  
Regional Climate ◽  
Horizontal Resolution ◽  
Monsoon Circulation ◽  
Data Sets ◽  
Data Set ◽  
Topographic Features ◽  
Temperature And Precipitation ◽  
Simulated Precipitation

The regional climate model HIRHAM has been applied over the Asian continent to simulate the Indian monsoon circulation under present-day conditions. The model is driven at the lateral and lower boundaries by European reanalysis (ERA40) data for the period from 1958 to 2001. Simulations with a horizontal resolution of 50 km are carried out to analyze the regional monsoon patterns. The focus in this paper is on the validation of the long-term summer monsoon climatology and its variability concerning circulation, temperature, and precipitation. Additionally, the monsoonal behavior in simulations for wet and dry years has been investigated and compared against several observational data sets. The results successfully reproduce the observations due to a realistic reproduction of topographic features. The simulated precipitation shows a better agreement with a high-resolution gridded precipitation data set over the central land areas of India and in the higher elevated Tibetan and Himalayan regions than ERA40.

scholarly journals Simulation and Comparison Between Mid-Holocene and Preindustrial Indian Summer Monsoon Circulation Using a Regional Climate Model

2012 ◽  
Vol 6 (1) ◽  
pp. 42-48 ◽  
Author(s):  
Stefan Polanski ◽  
Annette Rinke ◽  
Klaus Dethloff ◽  
Stephan J. Lorenz ◽  
Yongbo Wang ◽  
...  
Keyword(s):  
Indian Ocean ◽  
Regional Climate Model ◽  
Atmospheric Circulation ◽  
Summer Monsoon ◽  
Climate Model ◽  
Regional Climate ◽  
Horizontal Resolution ◽  
Climatic Conditions ◽  
Monsoon Circulation ◽  
Sst Anomalies

The regional climate model HIRHAM has been applied over the Asian continent from 0°N to 50°N and 42°E to 110°E to simulate the Indian monsoon circulation under past and present-day conditions. The model is driven at the lateral and lower boundaries by the atmospheric output fields of the global coupled Earth system model ECHAM5- JSBACH/MPIOM for 44-years-long time slices during the mid-Holocene and the preindustrial present-day climate. Simulations with a horizontal resolution of 50 km are carried out to analyze the regional monsoon patterns under different external solar forcing and climatic conditions. The focus is on the investigation of the HIRHAM simulated summer monsoon circulation and the comparison of the regional atmospheric circulation and precipitation patterns between the paleo- and the preindustrial climate. Due to mid-Holocene changes in the atmospheric circulation with a reduced and southward shifted monsoonal flow across Arabian Sea and Bay of Bengal, an increase of summer rainfall at the windward slopes of western and southern Himalayas as well as over southern India and decreased rainfall over central India appear which is in agreement with proxy-derived precipitation reconstructions. During the mid-Holocene as well as for the present-day climate the same driving mechanisms for the summer monsoon in extreme wet monsoon years related to regional SST anomalies in the Indian Ocean and convective processes can be verified. Positive (negative) SST anomalies in the northern Indian Ocean enhance (inhibit) the local convection associated with a deepening (weakening) of the low pressure and trigger wet (dry) rainfall anomalies.

scholarly journals Panama’s Current Climate Replicability in a Non-Hydrostatic Regional Climate Model Nested in an Atmospheric General Circulation Model

Atmosphere ◽  
2021 ◽  
Vol 12 (12) ◽  
pp. 1543
Author(s):  
Reinhardt Pinzón ◽  
Noriko N. Ishizaki ◽  
Hidetaka Sasaki ◽  
Tosiyuki Nakaegawa
Keyword(s):  
Regional Climate Model ◽  
Air Temperature ◽  
General Circulation ◽  
Climate Model ◽  
Regional Climate ◽  
Surface Air Temperature ◽  
Circulation Model ◽  
Horizontal Resolution ◽  
Temperature And Precipitation ◽  
Current Climate

To simulate the current climate, a 20-year integration of a non-hydrostatic regional climate model (NHRCM) with grid spacing of 5 and 2 km (NHRCM05 and NHRCM02, respectively) was nested within the AGCM. The three models did a similarly good job of simulating surface air temperature, and the spatial horizontal resolution did not affect these statistics. NHRCM02 did a good job of reproducing seasonal variations in surface air temperature. NHRCM05 overestimated annual mean precipitation in the western part of Panama and eastern part of the Pacific Ocean. NHRCM05 is responsible for this overestimation because it is not seen in MRI-AGCM. NHRCM02 simulated annual mean precipitation better than NHRCM05, probably due to a convection-permitting model without a convection scheme, such as the Kain and Fritsch scheme. Therefore, the finer horizontal resolution of NHRCM02 did a better job of replicating the current climatological mean geographical distributions and seasonal changes of surface air temperature and precipitation.

scholarly journals High resolution atmospheric reconstruction for Europe 1948–2012: coastDat2

2013 ◽  
Vol 6 (2) ◽  
pp. 779-809 ◽  
Author(s):  
B. Geyer
Keyword(s):  
Climate Model ◽  
Numerical Models ◽  
Regional Climate ◽  
Reanalysis Data ◽  
Data Sets ◽  
Data Set ◽  
Horizontal Grid ◽  
Global Reanalysis ◽  
Marine Climate

Abstract. The coastDat data sets were produced to give a consistent and homogeneous database mainly for assessing weather statistics and long-term changes for Europe, especially in data sparse regions. A sequence of numerical models was employed to reconstruct all aspects of marine climate (such as storms, waves, surges etc.) over many decades. Here, we describe the atmospheric part of coastDat2 (Geyer and Rockel, 2013, doi:10.1594/WDCC/coastDat-2_COSMO-CLM). It consists of a regional climate reconstruction for entire Europe, including Baltic and North Sea and parts of the Atlantic. The simulation was done for 1948 to 2012 with a regional climate model and a horizontal grid size of 0.22° in rotated coordinates. Global reanalysis data were used as forcing and spectral nudging was applied. To meet the demands on the coastDat data set about 70 variables are stored hourly.

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>

Impacts of different RCP scenarios on ALADIN-Climate regional climate model projections over Hungary

2020 ◽  
Author(s):  
Beatrix Bán ◽  
Gabriella Zsebeházi
Keyword(s):  
Regional Climate Model ◽  
Climate Model ◽  
Regional Climate ◽  
Horizontal Resolution ◽  
Observation Data ◽  
Rcp Scenarios ◽  
Local Climate ◽  
Temperature And Precipitation ◽  
Local Climate Change ◽  
Primary Basis

<p>The KlimAdat national project was started in 2016 to create a complex database of detailed meteorological information aiming to support local climate change impact studies in different sectors, adaptation strategies and related decision making. Besides observation data its primary basis will be ALADIN-Climate and REMO regional climate model simulations achieved by the Hungarian Meteorological Service and this set of projections will be extended by members of the Euro-CORDEX ensemble in order to quantify the projection uncertainties. <br>This study is focusing on analysis of the ALADIN-Climate model projections driven with RCP4.5 and RCP8.5 scenarios. Firstly, the CNRM-CM5 global model outputs were downscaled to 50 km horizontal resolution over the EURO-CORDEX domain with ALADIN-Climate Version 5.2. Then using these  results as lateral boundary conditions, 10 km experiments were prepared on a domain covering Central and South-Eastern Europe.<br>The presentation aims to introduce the behaviour of these simulations achieved by different scenarios and at different spatial resolution from the aspect of temperature and precipitation change over Hungary. Special attention will be put on the differences in extreme indices. Finally, our 10 km resolution simulations are compared with EURO-CORDEX results to specify their place in a larger ensemble.</p>

scholarly journals Regional climate model simulations as input for hydrological applications: evaluation of uncertainties

2005 ◽  
Vol 5 ◽  
pp. 119-125 ◽  
Author(s):  
S. Kotlarski ◽  
A. Block ◽  
U. Böhm ◽  
D. Jacob ◽  
K. Keuler ◽  
...  
Keyword(s):  
Climate Models ◽  
Climate Model ◽  
Reference Data ◽  
Regional Climate ◽  
Regional Climate Models ◽  
Mean Annual Precipitation ◽  
Data Sets ◽  
Data Set ◽  
Climate Model Simulations ◽  
Hydrological Applications

Abstract. The ERA15 Reanalysis (1979-1993) has been dynamically downscaled over Central Europe using 4 different regional climate models. The regional simulations were analysed with respect to 2m temperature and total precipitation, the main input parameters for hydrological applications. Model results were validated against three reference data sets (ERA15, CRU, DWD) and uncertainty ranges were derived. For mean annual 2 m temperature over Germany, the simulation bias lies between -1.1°C and +0.9°C depending on the combination of model and reference data set. The bias of mean annual precipitation varies between -31 and +108 mm/year. Differences between RCM results are of the same magnitude as differences between the reference data sets.

scholarly journals On the importance of observational data properties when assessing regional climate model performance of extreme precipitation

2013 ◽  
Vol 17 (11) ◽  
pp. 4323-4337 ◽  
Author(s):  
M. A. Sunyer ◽  
H. J. D. Sørup ◽  
O. B. Christensen ◽  
H. Madsen ◽  
D. Rosbjerg ◽  
...  
Keyword(s):  
Spatial Pattern ◽  
Observational Data ◽  
Extreme Precipitation ◽  
Climate Models ◽  
Climate Model ◽  
Regional Climate ◽  
Model Performance ◽  
Regional Climate Models ◽  
Data Sets ◽  
Data Set

Abstract. In recent years, there has been an increase in the number of climate studies addressing changes in extreme precipitation. A common step in these studies involves the assessment of the climate model performance. This is often measured by comparing climate model output with observational data. In the majority of such studies the characteristics and uncertainties of the observational data are neglected. This study addresses the influence of using different observational data sets to assess the climate model performance. Four different data sets covering Denmark using different gauge systems and comprising both networks of point measurements and gridded data sets are considered. Additionally, the influence of using different performance indices and metrics is addressed. A set of indices ranging from mean to extreme precipitation properties is calculated for all the data sets. For each of the observational data sets, the regional climate models (RCMs) are ranked according to their performance using two different metrics. These are based on the error in representing the indices and the spatial pattern. In comparison to the mean, extreme precipitation indices are highly dependent on the spatial resolution of the observations. The spatial pattern also shows differences between the observational data sets. These differences have a clear impact on the ranking of the climate models, which is highly dependent on the observational data set, the index and the metric used. The results highlight the need to be aware of the properties of observational data chosen in order to avoid overconfident and misleading conclusions with respect to climate model performance.

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