Regional climate model biases, their dependence on synoptic circulation biases and the potential for bias adjustment: a process‐oriented evaluation of the Austrian regional climate projections.

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&#8217; 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).Accurate and highly resolved climate projections require realistic modeling of soil hydrology. Thus, REMO&#8217;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.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.

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Extreme climate projections over the transboundary Koshi River Basin using a high resolution regional climate model

Advances in Climate Change Research ◽

10.1016/j.accre.2017.08.006 ◽

2017 ◽

Vol 8 (3) ◽

pp. 199-211 ◽

Cited By ~ 10

Author(s):

Rupak Rajbhandari ◽

Arun Bhakta Shrestha ◽

Santosh Nepal ◽

Shahriar Wahid ◽

Guo-Yu Ren

Keyword(s):

High Resolution ◽

Regional Climate Model ◽

River Basin ◽

Climate Model ◽

Regional Climate ◽

Climate Projections ◽

Extreme Climate ◽

Koshi River Basin

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Coordinated Global and Regional Climate Modeling*

Journal of Climate ◽

10.1175/jcli-d-15-0161.1 ◽

2015 ◽

Vol 29 (1) ◽

pp. 17-35 ◽

Cited By ~ 102

Author(s):

J. F. Scinocca ◽

V. V. Kharin ◽

Y. Jiao ◽

M. W. Qian ◽

M. Lazare ◽

...

Keyword(s):

Regional Climate Model ◽

Climate Model ◽

Climate Modeling ◽

Global Climate ◽

Global Climate Model ◽

Regional Climate ◽

Value Added ◽

Regional Climate Modeling ◽

Climate Projections ◽

Climate Modelling

Abstract A new approach of coordinated global and regional climate modeling is presented. It is applied to the Canadian Centre for Climate Modelling and Analysis Regional Climate Model (CanRCM4) and its parent global climate model CanESM2. CanRCM4 was developed specifically to downscale climate predictions and climate projections made by its parent global model. The close association of a regional climate model (RCM) with a parent global climate model (GCM) offers novel avenues of model development and application that are not typically available to independent regional climate modeling centers. For example, when CanRCM4 is driven by its parent model, driving information for all of its prognostic variables is available (including aerosols and chemical species), significantly improving the quality of their simulation. Additionally, CanRCM4 can be driven by its parent model for all downscaling applications by employing a spectral nudging procedure in CanESM2 designed to constrain its evolution to follow any large-scale driving data. Coordination offers benefit to the development of physical parameterizations and provides an objective means to evaluate the scalability of such parameterizations across a range of spatial resolutions. Finally, coordinating regional and global modeling efforts helps to highlight the importance of assessing RCMs’ value added relative to their driving global models. As a first step in this direction, a framework for identifying appreciable differences in RCM versus GCM climate change results is proposed and applied to CanRCM4 and CanESM2.

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Climate Change Explorer: Extracting localized data for developing Climate Services

10.5194/egusphere-egu21-12336 ◽

2021 ◽

Author(s):

Christine Nam ◽

Bente Tiedje ◽

Susanne Pfeifer ◽

Diana Rechid ◽

Daniel Eggert

Keyword(s):

Climate Change ◽

Regional Climate Model ◽

Climate Model ◽

Regional Climate ◽

Heavy Precipitation ◽

Climate Projections ◽

Climate Data ◽

Climate Services ◽

Mitigation And Adaptation ◽

Data Formats

Everyone, politicians, public administrations, business owners, and citizens want to know how climate changes will affect them locally. Having such knowledge offers everyone the opportunity to make informed choices and take action towards mitigation and adaptation.&#160;In order to develop locally relevant climate service products and climate advisory services, as we do at GERICS, we must extract localized climate change information from Regional Climate Model ensemble simulations.&#160;Common challenges associated with developing such services include the transformation of petabytes of data from physical quantities such as precipitation, temperature, or wind, into user-applicable quantities such as return periods of heavy precipitation, e.g. for legislative or construction design frequency. Other challenges include the technical and physical barriers in the use and interpretation of climate data, due to large data volume, unfamiliar software and data formats, or limited technical infrastructure. The interpretation of climate data also requires scientific background knowledge, which limit or influence the interpretation of results.&#160;These barriers hinder the efficient and effective transformation of big data into user relevant information in a timely and reliable manner. To enable our society to adapt and become more resilient to climate change, we must overcome these barriers. In the Helmholtz funded Digital Earth project we are tackling these challenges by developing a Climate Change Workflow.&#160;In the scope of this Workflow, the user can easily define a region of interest and extract the relevant climate data from the simulations available at the Earth System Grid Federation (ESGF). Following which, a general overview of the projected changes, in precipitation for example, for multiple climate projections is presented. It conveys the bandwidth, i.e. the minimum/maximum range by an ensemble of regional climate model projections. We implemented the sketched workflow in a web-based tool called The Climate Change Explorer. It addresses barriers associated with extracting locally relevant climate data from petabytes of data, in unfamilar data formats, and deals with interpolation issues, using a more intuitive and user-friendly web interface.&#160;Ultimately, the Climate Change Explorer provides concise information on the magnitude of projected climate change and the range of these changes for individually defined regions, such as found in GERICS &#8216;Climate Fact Sheets&#8217;. This tool has the capacity to also improve other workflows of climate services, allowing them to dedicate more time in deriving user relevant climate indicies; enabling politicians, public administrations, and businesses to take action.

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Evaluation of Agriculture-Related Climate Indices in Hindcast COSMO-CLM Simulations over Central Europe

Environmental Sciences Proceedings ◽

10.3390/ecas2020-08464 ◽

2020 ◽

Vol 4 (1) ◽

pp. 27

Author(s):

Huan Zhang ◽

Merja H. Tölle

Keyword(s):

Regional Climate Model ◽

Central Europe ◽

Climate Model ◽

Crop Yields ◽

Regional Climate ◽

Growing Season ◽

Horizontal Resolution ◽

Climate Indices ◽

Climate Projections ◽

Growing Season Length

High horizontal resolution regional climate model simulations serve as forcing data for crop and dynamic vegetation models, for generating possible scenarios of the future effects of climate change on crop yields and pollinators. Here, we performed convection-permitting hindcast simulations with the regional climate model COSMO5.0-CLM15 (CCLM) from 1979 to 2015, and the first year was considered as a spin-up period. The model was driven with hourly ERA5 data, which were the latest climate reanalysis product by ECMWF, and directly downscaled to a 3 km horizontal resolution over Central Europe. The land-use classes were described by ECOCLIMAP, and the soil type and depth were described by HWSD. The evaluation was carried out in terms of temperature, precipitation, and climate indices, comparing CCLM output with the gridded observational dataset HYRAS from the German Weather Service. While CCLM inherits a warm and dry summer bias found in its parent model, it reproduces the main features of the recent past climate of Central Europe, including the seasonal mean climate patterns and probability density distributions. Furthermore, the model reproduced climate indices for temperature like growing season length, growing season start date, number of summer days. The results highlighted the possibility of directly downscaling ERA5 data with regional climate models, avoiding the multiple nesting approach and high computational costs. This study adds confidence to convection-permitting climate projections of future changes in agricultural climate indices.

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Robustness of future atmospheric circulation changes over the EURO-CORDEX domain

Climate Dynamics ◽

10.1007/s00382-021-06069-0 ◽

2021 ◽

Author(s):

Tugba Ozturk ◽

Dominic Matte ◽

Jens Hesselbjerg Christensen

Keyword(s):

Wind Speed ◽

Atmospheric Circulation ◽

Central Europe ◽

Climate Model ◽

Global Climate Model ◽

Regional Climate ◽

Climate Projections ◽

Future Change ◽

Ensemble Mean ◽

Regional Climate Projections

AbstractEuropean climate is associated with variability and changes in the mid-latitude atmospheric circulation. In this study, we aim to investigate potential future change in circulation over Europe by using the EURO-CORDEX regional climate projections at 0.11° grid mesh. In particular, we analyze future change in 500-hPa geopotential height (Gph), 500-hPa wind speed and mean sea level pressure (MSLP) addressing different warming levels of 1 °C, 2 °C and 3 °C, respectively. Simple scaling with the global mean temperature change is applied to the regional climate projections for monthly mean 500-hPa Gph and 500-hPa wind speed. Results from the ensemble mean of individual models show a robust increase in 500-hPa Gph and MSLP in winter over Mediterranean and Central Europe, indicating an intensification of anticyclonic circulation. This circulation change emerges robustly in most simulations within the coming decade. There are also enhanced westerlies which transport warm and moist air to the Mediterranean and Central Europe in winter and spring. It is also clear that, models showing different responses to circulation depend very much on the global climate model ensemble member in which they are nested. For all seasons, particularly autumn, the ensemble mean is much more correlated with the end of the century than most of the individual models. In general, the emergence of a scaled pattern appears rather quickly.

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Balanced estimate and uncertainty assessment of European climate change using the large EURO-CORDEX regional climate model ensemble

10.5194/esd-2021-8 ◽

2021 ◽

Author(s):

Guillaume Evin ◽

Samuel Somot ◽

Benoit Hingray

Keyword(s):

Regional Climate Model ◽

Climate Models ◽

Climate Model ◽

Global Climate ◽

Regional Climate ◽

Uncertainty Assessment ◽

Global Climate Models ◽

Climate Projections ◽

Total Uncertainty ◽

Near Surface

Abstract. Large Multiscenarios Multimodel Ensembles (MMEs) of regional climate model (RCM) experiments driven by Global Climate Models (GCM) are made available worldwide and aim at providing robust estimates of climate changes and associated uncertainties. Due to many missing combinations of emission scenarios and climate models leading to sparse Scenario-GCM-RCM matrices, these large ensembles are however very unbalanced, which makes uncertainty analyses impossible with standard approaches. In this paper, the uncertainty assessment is carried out by applying an advanced statistical approach, called QUALYPSO, to a very large ensemble of 87 EURO-CORDEX climate projections, the largest ensemble ever produced for regional projections in Europe. This analysis provides i) the most up-to-date and balanced estimates of mean changes for near-surface temperature and precipitation in Europe, ii) the total uncertainty of projections and its partition as a function of time, and iii) the list of the most important contributors to the model uncertainty. For changes of total precipitation and mean temperature in winter (DJF) and summer (JJA), the uncertainty due to RCMs can be as large as the uncertainty due to GCMs at the end of the century (2071–2099). Both uncertainty sources are mainly due to a small number of individual models clearly identified. Due to the highly unbalanced character of the MME, mean estimated changes can drastically differ from standard average estimates based on the raw ensemble of opportunity. For the RCP4.5 emission scenario in Central-Eastern Europe for instance, the difference between balanced and direct estimates are up to 0.8 °C for summer temperature changes and up to 20 % for summer precipitation changes at the end of the century.

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Sensitivity studies with the Regional Climate Model COSMO-CLM 5.0 over the CORDEX Central Asia Domain

10.5194/gmd-2019-22 ◽

2019 ◽

Author(s):

Emmanuele Russo ◽

Ingo Kirchner ◽

Stephan Pfahl ◽

Martijn Schaap ◽

Ulrich Cubasch

Keyword(s):

Regional Climate Model ◽

Central Asia ◽

Climate Models ◽

Climate Model ◽

Reference Model ◽

Developing Economies ◽

Regional Climate ◽

Model Performance ◽

Mitigation Strategies ◽

Climate Projections

Abstract. Due to its extension, geography and the presence of several under-developed or developing economies, the Central Asia domain of the Coordinated Regional climate Downscaling Experiment (CORDEX) is one of the most vulnerable regions on Earth to the effects of climate changes. Reliable information on potential future changes with high spatial resolution acquire significant importance for the development of effective adaptation and mitigation strategies for the region. In this context, Regional Climate Models (RCMs) play a fundamental role. In this paper, the results of a set of sensitivity experiments with the regional climate model COSMO-CLM version 5.0, for the Central Asia CORDEX domain, are presented. Starting from a reference model setup, general model performance is evaluated for present-days, testing the effects of a set of singular physical parameterizations and their mutual interaction on the simulation of monthly and seasonal values of three variables that are important for impact studies: 2-meter temperature, precipitation and diurnal temperature range. The final goal of this study is two-fold: having a general overview of model performance and its uncertainties for the considered region and determining at the same time an optimal model configuration. Results show that the model presents remarkable deficiencies over different areas of the domain. The combined change of the albedo taking into consideration the ratio of forest fractions and the soil conductivity taking into account the ratio of liquid water and ice in the soil, allows to achieve the best improvements in model performance in terms of climatological means. Importantly, the model seems to be particularly sensitive to those parameterizations that deal with soil and surface features, and that could positively affect the repartition of incoming radiation. The results for the mean climate appear to be independent of the observational dataset used for evaluation and of the boundary data employed to force the simulations. On the other hand, due to the large uncertainties in the variability estimates from observations, the use of different boundaries and the model internal variability, it has not been possible to rank the different simulations according to their representation of the monthly variability. This work is the first ever sensitivity study of an RCM for the CORDEX Central Asia domain and its results are of fundamental importance for further model development and for future climate projections over the area.

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More realistic land‐use and vegetation parameters in a regional climate model reduce model biases over China

International Journal of Climatology ◽

10.1002/joc.6110 ◽

2019 ◽

Vol 39 (12) ◽

pp. 4825-4837

Author(s):

Jiaojiao Gou ◽

Fei Wang ◽

Kai Jin ◽

Xingmin Mu ◽

Deliang Chen

Keyword(s):

Land Use ◽

Regional Climate Model ◽

Climate Model ◽

Regional Climate ◽

Model Biases ◽

Vegetation Parameters

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Assessment of climate change exposure of tourism in Hungary using observations and regional climate model data

Hungarian Geographical Bulletin ◽

10.15201/hungeobull.70.3.2 ◽

2021 ◽

Vol 70 (3) ◽

pp. 215-231

Author(s):

Attila Kovács ◽

◽

Andrea Király ◽

Keyword(s):

Climate Change ◽

Regional Climate Model ◽

Climate Model ◽

Regional Climate ◽

Strategy Development ◽

Climate Index ◽

Climate Projections ◽

Climate Conditions ◽

The Future ◽

Tourism Climate

Climate constitutes key resources for tourism since it influences the range of tourism activities and the development of tourism supply. Tourism is highly sensitive to changes in climate elements. It is extremely important for adaptation strategy-making to explore whether the tourism climate conditions in a given region and at a specific time are appropriate and how they may change in the future. This is described by the exposure of the tourism sector to climate conditions and climate change. In this study, we analyse the exposure of tourism for Hungary on a district level and every month (from March to November) with the help of the modified Tourism Climate Index. First, the present conditions are evaluated based on a gridded observational database CarpatClim-HU, which forms the basis for assessing the future conditions. Afterwards, the expected future circumstances are analysed using regional climate model outputs. In order to interpret the uncertainties of the climate projections properly, we use two different model results (HIRHAM5 and RACMO22E) relying on two emission scenarios (RCP4.5 and RCP8.5). The results have demonstrated that the most favourable conditions are found in spring (MAM) and autumn (SON), while in summer (JJA) a decline in climate potential is observed. According to the future tendencies, generally, a decline is expected between May and September, but the other investigated months usually bring an improvement. For a given emission scenario, the expected trend is quite similar for the two model experiments, while for a given climate model, the use of RCP8.5 scenario indicates larger changes than RCP4.5. The results prove that climate change will have an obvious impact on tourism potential in Hungary, and therefore tourism strategy development has to take into account this effect more than before.

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