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.

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

2014 ◽  
Vol 6 (1) ◽  
pp. 147-164 ◽  
Author(s):  
B. Geyer
Keyword(s):  
Climate Model ◽  
Numerical Models ◽  
Regional Climate ◽  
Reanalysis Data ◽  
Data Sets ◽  
Data Set ◽  
European Continent ◽  
Atmospheric Data ◽  
The Baltic ◽  
Horizontal Grid

Abstract. The coastDat data sets were produced to give a consistent and homogeneous database mainly for assessing weather statistics and climate changes since 1948, e.g., in frequencies of extremes 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. The acronym coastDat stands for the set of consistent ocean and atmospheric data, where the atmospheric data where used as forcing for the reconstruction of the sea state. 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 the entire European continent, including the Baltic Sea and North Sea and parts of the Atlantic. The simulation was done for 1948 to 2012 with the regional climate model COSMO-CLM (CCLM) and a horizontal grid size of 0.22 degree in rotated coordinates. Global reanalysis data of NCEP1 were used as forcing and spectral nudging was applied. To meet the demands on the coastDat data set about 70 variables are stored hourly.

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.

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 Sensitivity of lake ice regimes to climate change in the nordic region

2013 ◽  
Vol 7 (2) ◽  
pp. 743-788 ◽  
Author(s):  
S. Gebre ◽  
T. Boissy ◽  
K. Alfredsen
Keyword(s):  
Climate Model ◽  
Regional Climate ◽  
Reanalysis Data ◽  
Ice Thickness ◽  
Annual Maximum ◽  
Climate Scenarios ◽  
Lake Ice ◽  
Data Set ◽  
Nordic Region ◽  
Baltic Countries

Abstract. A one-dimensional process-based multi-year lake ice model, MyLake, was used to simulate lake ice phenology and annual maximum lake ice thickness for the Nordic region comprising Fennoscandia and the Baltic countries. The model was first tested and validated using observational meteorological forcing on a candidate lake (Lake Atnsjøen) and using downscaled ERA-40 reanalysis data set. To simulate ice conditions for the contemporary period of 1961–2000, the model was driven by gridded meteorological forcings from ERA-40 global reanalysis data downscaled to a 25 km resolution using the Rossby Center Regional Climate Model (RCA). The model was then forced with two future climate scenarios from the RCA driven by two different GCMs based on the SRES A1B emissions scenario. The two climate scenarios correspond to two future time periods namely the 2050s (2041–2070) and the 2080s (2071–2100). To take into account the influence of lake morphometry, simulations were carried out for four different hypothetical lake depths (5 m, 10 m, 20 m, 40 m) placed at each of the 3708 grid cells. Based on a comparison of the mean predictions in the future 30 yr periods with the control (1961–1990) period, ice cover durations in the region will be shortened by 1 to 11 weeks in 2041–2070, and 3 to 14 weeks in 2071–2100. Annual maximum lake ice thickness, on the other hand, will be reduced by a margin of up to 60 cm by 2041–2070 and up to 70 cm by 2071–2100. The simulated changes in lake ice characteristics revealed that the changes are less dependent on lake depths though there are slight differences. The results of this study provide a~regional perspective of anticipated changes in lake ice regimes due to climate warming across the study area by the middle and end of this century.

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.

scholarly journals Synoptic-to-Regional-Scale Analysis of Rainfall in the Atacama Desert (18°–26°S) Using a Long-Term Simulation with WRF

2021 ◽  
Vol 149 (1) ◽  
pp. 91-112
Author(s):  
Mark Reyers ◽  
Christoph Boehm ◽  
Leon Knarr ◽  
Yaping Shao ◽  
Susanne Crewell
Keyword(s):  
Moisture Transport ◽  
Climate Model ◽  
Regional Climate ◽  
Regional Scale ◽  
Atacama Desert ◽  
Summer Rainfall ◽  
Reanalysis Data ◽  
Rainfall Events ◽  
Convective Systems

AbstractIn this study, reanalysis data and a long-term simulation with the regional climate model WRF (1982–2017; 10 km resolution) is used to analyze synoptic and regional processes associated with rainfall events in the Atacama Desert. Five composites, each with 10 WRF-simulated rainfall events, are studied. They are selected based on a clustering and comprise the top winter events in South Atacama (23°–26°S), Southeast Atacama, and North Atacama (18°–23°S), and the top summer events in North Atacama and Northeast Atacama. Winter rainfall events in South Atacama are mostly associated with strong low pressure systems over the southeast Pacific and atmospheric rivers at their foreside, while cutoff lows occurring anomalously far north facilitate strong rainfall in North Atacama. Accordingly, tropical continental areas and the remote tropical and subtropical Pacific are identified as primary moisture sources, and moisture transport toward the Atacama Desert mainly takes place in the free troposphere (above 800 hPa). Strong summer rainfall events in North Atacama and Northeast Atacama are associated with a southward displaced Bolivian high. During rainfall events in North Atacama the high is shifted westward when compared to the Northeast Atacama events. Consequently, northern Chile is located at the northern periphery of the Bolivian high and the resulting strong easterlies may push strong convective systems from the Altiplano, toward the Atacama coast. Detailed analyses of individual rainfall events reveal that the most important synoptic patterns associated with rainfall not only control the synoptic-scale moisture transport into the Atacama Desert, but also decisively influence the regional atmospheric circulation.

scholarly journals Long-term Variances of Heavy Precipitation across Central Europe using a Large Ensemble of Regional Climate Model Simulations

2019 ◽  
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 ◽  
Heavy Precipitation ◽  
Data Sets ◽  
Observation Data ◽  
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. 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, a large ensemble of decadal hindcasts, and also projections for the upcoming decade. Global reanalysis for the 20th century with a horizontal resolution of more than 100 km have been dynamically downscaled with a regional climate model (COSMO-CLM) towards a higher resolution of 25 km. The new data sets are first filtered using a dry-day adjustment. The simulations show a good agreement with observations for both statistical distributions and time series. Differences mainly appear in areas with sparse observation data. The temporal evolution during the past 60 years is well captured. The results reveal some long-term variability with phases of increased and decreased heavy precipitation. The overall trend varies between the investigation areas but is significant. The projections for the upcoming decade show ongoing tendencies with increased precipitation for upper percentiles. The presented RCM ensemble not only allows for more robust statistics in general, in particular it is suitable for a better estimation of extreme values.

scholarly journals Seasonal effects in the long-term correction of short-term wind measurements using reanalysis data

2021 ◽  
Author(s):  
Alexander Basse ◽  
Doron Callies ◽  
Anselm Grötzner ◽  
Lukas Pauscher
Keyword(s):  
Reference Data ◽  
Measurement Data ◽  
Reanalysis Data ◽  
Seasonal Effects ◽  
Data Sets ◽  
Data Set ◽  
Abstract Measure ◽  
Wind Measurements ◽  
Theoretical Considerations

Abstract. Measure-Correlate-Predict (MCP) approaches are often used to correct wind measurements to the long-term wind conditions on site. This paper investigates systematic errors in MCP-based long-term corrections which occur if the measurement on site covers only a few months (seasonal biases). In this context, two common linear MCP methods are tested and compared, namely Variance Ratio and Linear Regression with Residuals. Wind measurement data from 18 sites with different terrain complexity in Germany are used (measurement heights between 100 and 140 m). Six different reanalysis data sets serve as the reference (long-term) wind data in the MCP calculations. Besides experimental results, theoretical considerations are presented which provide the mathematical background for understanding the observations. General relationships are derived which trace the seasonal biases to the mechanics of the methods and the properties of the reanalysis data sets. This allows the transfer of the results of this study to different measurement durations, other reference data sets and other regions of the world. In this context, it is shown both theoretically and experimentally that the results do not only depend on the selected reference data set but also significantly change with the choice of the MCP method.

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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