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

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.

scholarly journals Sensitivity studies with the regional climate model COSMO-CLM 5.0 over the CORDEX Central Asia Domain

2019 ◽  
Vol 12 (12) ◽  
pp. 5229-5249 ◽  
Author(s):  
Emmanuele Russo ◽  
Ingo Kirchner ◽  
Stephan Pfahl ◽  
Martijn Schaap ◽  
Ulrich Cubasch
Keyword(s):  
Regional Climate Model ◽  
Central Asia ◽  
Climate Model ◽  
Reference Model ◽  
Developing Economies ◽  
Regional Climate ◽  
Model Performance ◽  
Mitigation Strategies ◽  
Climate Projections ◽  
Near Surface

Abstract. Due to its extension, geography and the presence of several underdeveloped 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 the present day, testing the effects of singular changes in the model physical configuration and their mutual interaction with the simulation of monthly and seasonal values of three variables that are important for impact studies: near-surface 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 one 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 analyses also show that improvements in model performance are not achievable for all domain subregions and variables, and they are the result of a compensation effect in the different cases. The proposed better performing configuration in terms of mean climate leads to similar positive improvements when considering different observational data sets and 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.

Comparison of different infiltration schemes in the regional climate model REMO

2021 ◽  
Author(s):  
Daniel Abel ◽  
Katrin Ziegler ◽  
Felix Pollinger ◽  
Heiko Paeth
Keyword(s):  
Regional Climate Model ◽  
Climate Models ◽  
Climate Model ◽  
Root Zone ◽  
Regional Climate ◽  
Climate Projections ◽  
Soil Hydrology ◽  
Spatiotemporal Resolution ◽  
Development Fund ◽  
Near Surface

<p>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’ 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).</p><p>Accurate and highly resolved climate projections require realistic modeling of soil hydrology. Thus, REMO’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.</p><p>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.</p>

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

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.

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

2021 ◽  
Vol 12 (4) ◽  
pp. 1543-1569
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 multiscenario multimodel ensembles (MMEs) of regional climate model (RCM) experiments driven by global climate models (GCMs) 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, however, are 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 MME based on regional climate models ever produced in Europe. This analysis provides a detailed description of this MME, including (i) 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 in 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 is up to 0.8 ∘C for summer temperature changes and up to 20 % for summer precipitation changes at the end of the century.

scholarly journals Dynamical downscaling for regional climate projection in Central Asia based on bias-corrected multiple GCMs

2021 ◽  
Author(s):  
Yuan Qiu ◽  
Jinming Feng ◽  
Zhongwei Yan ◽  
Jun Wang
Keyword(s):  
Climate Change ◽  
Central Asia ◽  
Climate Changes ◽  
Climate Models ◽  
Climate Model ◽  
Dynamical Downscaling ◽  
Regional Climate ◽  
High Elevation ◽  
Global Climate Models ◽  
Climate Projections

Abstract Central Asia (CA) is among the most vulnerable regions to climate change due to the fragile ecosystems, frequent natural hazards, strained water resources, and accelerated glacier melting, which underscores the need to achieve robust projection of regional climate change. In this study, we applied three bias-corrected global climate models (GCMs) to conduct 9km-resolution regional climate simulations in CA for the present (1986–2005) and future (2031–2050) periods. Dynamical downscaling based on multiple bias-corrected GCM outputs obtains numerous added values not only in reproducing the historical climate but also in projecting the climate changes in CA, in comparison to the original GCMs. The regional climate model (RCM) simulations indicate significant warming over CA in the near-term future, with the regional mean increase of annual daily mean temperature (Tmean) in a range of 1.63–2.01℃, relative to the present period. This increase is expected to be higher north of ~ 45°N in each season except summer and the high-elevation areas have a weaker warming signal than the plains through the year. The season with the largest warming rate is not consistent among the RCM simulations, highlighting the necessity of using multiple GCMs as the boundary conditions to give a range of the projected climate changes. A slight increase in annual precipitation is consistently projected in most plain areas, although the changes over few areas are statistically significant. The climate projections presented here serve as a robust scientific basis for assessment of future risk from climate change in CA.

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>

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