The future climate characteristics of the Carpathian Basin based on a regional climate model mini-ensemble

Abstract. Four regional climate models (RCMs) were adapted in Hungary for the dynamical downscaling of the global climate projections over the Carpathian Basin: (i) the ALADIN-Climate model developed by Météo France on the basis of the ALADIN short-range modelling system; (ii) the PRECIS model available from the UK Met Office Hadley Centre; (iii) the RegCM model originally developed at the US National Center for Atmospheric Research, is maintained at the International Centre for Theoretical Physics in Trieste; and (iv) the REMO model developed by the Max Planck Institute for Meteorology in Hamburg. The RCMs are different in terms of dynamical model formulation, physical parameterisations; moreover, in the completed simulations they use different spatial resolutions, integration domains and lateral boundary conditions for the scenario experiments. Therefore, the results of the four RCMs can be considered as a small ensemble providing information about various kinds of uncertainties in the future projections over the target area, i.e., Hungary. After the validation of the temperature and precipitation patterns against measurements, mean changes and some extreme characteristics of these patterns (including their statistical significance) have been assessed focusing on the periods of 2021–2050 and 2071–2100 relative to the 1961–1990 model reference period. The ensemble evaluation indicates that the temperature-related changes of the different RCMs are in good agreement over the Carpathian Basin and these tendencies manifest in the general warming conditions. The precipitation changes cannot be identified so clearly: seasonally large differences can be recognised among the projections and between the two periods. An overview is given about the results of the mini-ensemble and special emphasis is put on estimating the uncertainties in the simulations for Hungary.

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

10.21203/rs.3.rs-276894/v1 ◽

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.

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Future humidity extremes in an urban-rural context - using regional climate model projections down to convection permitting scales for Berlin

10.5194/egusphere-egu21-12054 ◽

2021 ◽

Author(s):

Gaby S. Langendijk ◽

Diana Rechid ◽

Daniela Jacob

Keyword(s):

Climate Change ◽

Urban Areas ◽

Climate Models ◽

Climate Model ◽

Regional Climate ◽

Climate Change Impacts ◽

Regional Climate Models ◽

Climate Projections ◽

Climate Data ◽

Urban Rural

Urban areas are prone to climate change impacts. A transition towards sustainable and climate-resilient urban areas is relying heavily on useful, evidence-based climate information on urban scales. However, current climate data and information produced by urban or climate models are either not scale compliant for cities, or do not cover essential parameters and/or urban-rural interactions under climate change conditions. Furthermore, although e.g. the urban heat island may be better understood, other phenomena, such as moisture change, are little researched. Our research shows the potential of regional climate models, within the EURO-CORDEX framework, to provide climate projections and information on urban scales for 11km and 3km grid size. The city of Berlin is taken as a case-study. The results on the 11km spatial scale show that the regional climate models simulate a distinct difference between Berlin and its surroundings for temperature and humidity related variables. There is an increase in urban dry island conditions in Berlin towards the end of the 21st century. To gain a more detailed understanding of climate change impacts, extreme weather conditions were investigated under a 2&#176;C global warming and further downscaled to the 3km scale. This enables the exploration of differences of the meteorological processes between the 11km and 3km scales, and the implications for urban areas and its surroundings. The overall study shows the potential of regional climate models to provide climate change information on urban scales.

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Comparison of different infiltration schemes in the regional climate model REMO

10.5194/egusphere-egu21-9874 ◽

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

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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Decomposition of Future Moisture Flux Changes over the Tibetan Plateau Projected by Global and Regional Climate Models

Journal of Climate ◽

10.1175/jcli-d-19-0200.1 ◽

2019 ◽

Vol 32 (20) ◽

pp. 7037-7053

Author(s):

Hongwen Zhang ◽

Yanhong Gao ◽

Jianwei Xu ◽

Yu Xu ◽

Yingsha Jiang

Keyword(s):

Tibetan Plateau ◽

Climate Model ◽

Dynamical Downscaling ◽

Regional Climate ◽

Moisture Flux ◽

Historical Period ◽

The Tibetan Plateau ◽

Dynamic Component ◽

Coarse Resolution ◽

The Future

Abstract To meet the requirement of high-resolution datasets for many applications, a dynamical downscaling approach using a regional climate model (the WRF Model) driven by a global climate model (CCSM4) has been adopted. This study focuses on projections of future moisture flux changes over the Tibetan Plateau (TP). First, the downscaling results for the historical period (1980–2005) are evaluated for precipitation P, evaporation E, and precipitation minus evaporation P − E against Global Land Data Assimilation System (GLDAS) data. The mechanism of P − E changes is analyzed by decomposition into dynamic, thermodynamic, and transient eddy components. Whether the historical period changes and mechanisms continue into the future (2010–2100) is investigated using the WRF and CCSM model projections under the RCP4.5 and RCP8.5 scenarios. Compared with coarse-resolution forcing, downscaling was found to better reproduce the historical spatial patterns and seasonal mean of annual average P, E, and P − E over the TP. WRF projects a diverse spatial variation of P − E changes, with an increase in the northern TP and a decrease in the southern TP, compared with the uniform increase in CCSM. The dynamic component dominates P − E changes for the historical period in both the CCSM and WRF projections. In the future, however, the thermodynamic component in CCSM dominates P − E changes under RCP4.5 and RCP8.5 from the near-term (2010–39) to the long-term (2070–99) future. Unlike the CCSM projections, the WRF projections reproduce the mechanism seen in the historical period—that is, the dynamic component dominates P − E changes. Furthermore, future P − E changes in the dynamical downscaling are less sensitive to warming than its coarse-resolution forcing.

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Climate Model Evaluation in the Presence of Observational Uncertainty: Precipitation Indices over the Contiguous United States

Journal of Hydrometeorology ◽

10.1175/jhm-d-18-0230.1 ◽

2019 ◽

Vol 20 (7) ◽

pp. 1339-1357 ◽

Cited By ~ 9

Author(s):

Peter B. Gibson ◽

Duane E. Waliser ◽

Huikyo Lee ◽

Baijun Tian ◽

Elias Massoud

Keyword(s):

United States ◽

Model Evaluation ◽

Climate Models ◽

Climate Model ◽

Dynamical Downscaling ◽

Daily Precipitation ◽

Regional Climate ◽

Climate Model Evaluation ◽

Observational Uncertainty ◽

Precipitation Indices

Abstract Climate model evaluation is complicated by the presence of observational uncertainty. In this study we analyze daily precipitation indices and compare multiple gridded observational and reanalysis products with regional climate models (RCMs) from the North American component of the Coordinated Regional Climate Downscaling Experiment (NA-CORDEX) multimodel ensemble. In the context of model evaluation, observational product differences across the contiguous United States (CONUS) are also deemed nontrivial for some indices, especially for annual counts of consecutive wet days and for heavy precipitation indices. Multidimensional scaling (MDS) is used to directly include this observational spread into the model evaluation procedure, enabling visualization and interpretation of model differences relative to a “cloud” of observational uncertainty. Applying MDS to the evaluation of NA-CORDEX RCMs reveals situations of added value from dynamical downscaling, situations of degraded performance from dynamical downscaling, and the sensitivity of model performance to model resolution. On precipitation days, higher-resolution RCMs typically simulate higher mean and extreme precipitation rates than their lower-resolution pairs, sometimes improving model fidelity with observations. These results document the model spread and biases in daily precipitation extremes across the full NA-CORDEX model ensemble. The often-large divergence between in situ observations, satellite data, and reanalysis, shown here for CONUS, is especially relevant for data-sparse regions of the globe where satellite and reanalysis products are extensively relied upon. This highlights the need to carefully consider multiple observational products when evaluating climate models.

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Selection of Bias Correction Methods to Assess the Impact of Climate Change on Flood Frequency Curves

Water ◽

10.3390/w11112266 ◽

2019 ◽

Vol 11 (11) ◽

pp. 2266 ◽

Cited By ~ 4

Author(s):

Enrique Soriano ◽

Luis Mediero ◽

Carlos Garijo

Keyword(s):

Climate Change ◽

Bias Correction ◽

Climate Models ◽

Climate Model ◽

Control Period ◽

Flood Frequency ◽

Climate Projections ◽

The Future ◽

Flood Quantiles ◽

The Impact

Climate projections provided by EURO-CORDEX predict changes in annual maximum series of daily rainfall in the future in some areas of Spain because of climate change. Precipitation and temperature projections supplied by climate models do not usually fit exactly the statistical properties of the observed time series in the control period. Bias correction methods are used to reduce such errors. This paper seeks to find the most adequate bias correction techniques for temperature and precipitation projections that minimizes the errors between observations and climate model simulations in the control period. Errors in flood quantiles are considered to identify the best bias correction techniques, as flood quantiles are used for hydraulic infrastructure design and safety assessment. In addition, this study aims to understand how the expected changes in precipitation extremes and temperature will affect the catchment response in flood events in the future. Hydrological modelling is required to characterize rainfall-runoff processes adequately in a changing climate, in order to estimate flood changes expected in the future. Four catchments located in the central-western part of Spain have been selected as case studies. The HBV hydrological model has been calibrated in the four catchments by using the observed precipitation, temperature and streamflow data available on a daily scale. Rainfall has been identified as the most significant input to the model, in terms of its influence on flood response. The quantile mapping polynomial correction has been found to be the best bias correction method for precipitation. A general reduction in flood quantiles is expected in the future, smoothing the increases identified in precipitation quantiles by the reduction of soil moisture content in catchments, due to the expected increase in temperature and decrease in mean annual precipitations.

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Quantification of the Expected Changes in Annual Maximum Daily Precipitation Quantiles under Climate Change in the Iberian Peninsula

Proceedings ◽

10.3390/ecws-3-05819 ◽

2018 ◽

Vol 7 (1) ◽

pp. 23 ◽

Cited By ~ 1

Author(s):

Carlos Garijo ◽

Luis Mediero

Keyword(s):

Climate Change ◽

Iberian Peninsula ◽

Climate Models ◽

Climate Model ◽

Global Climate ◽

Global Climate Model ◽

Regional Climate ◽

Control Period ◽

Regional Climate Models ◽

The Future

Climate model projections can be used to assess the expected behaviour of extreme precipitations in the future due to climate change. The European part of the Coordinated Regional Climate Downscalling Experiment (EURO-CORDEX) provides precipitation projections for the future under various representative concentration pathways (RCPs) through regionalised Global Climate Model (GCM) outputs by a set of Regional Climate Models (RCMs). In this work, 12 combinations of GCM and RCM under two scenarios (RCP 4.5 and RCP 8.5) supplied by the EURO-CORDEX are analysed for the Iberian Peninsula. Precipitation quantiles for a set of probabilities of non-exceedance are estimated by using the Generalized Extreme Value (GEV) distribution and L-moments. Precipitation quantiles expected in the future are compared with the precipitation quantiles in the control period for each climate model. An approach based on Monte Carlo simulations is developed in order to assess the uncertainty from the climate model projections. Expected changes in the future are compared with the sampling uncertainty in the control period. Thus, statistically significant changes are identified. The higher the significance threshold, the fewer cells with significant changes are identified. Consequently, a set of maps are obtained in order to assist the decision-making process in subsequent climate change studies.

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Do Convection-Permitting Regional Climate Models Improve Projections of Future Precipitation Change?

Bulletin of the American Meteorological Society ◽

10.1175/bams-d-15-0004.1 ◽

2017 ◽

Vol 98 (1) ◽

pp. 79-93 ◽

Cited By ~ 121

Author(s):

Elizabeth J. Kendon ◽

Nikolina Ban ◽

Nigel M. Roberts ◽

Hayley J. Fowler ◽

Malcolm J. Roberts ◽

...

Keyword(s):

Climate Change ◽

Large Scale ◽

Climate Models ◽

Climate Model ◽

Global Climate Model ◽

Regional Climate ◽

Regional Climate Models ◽

Climate Projections ◽

Convective Storms ◽

Wide Range

Abstract Regional climate projections are used in a wide range of impact studies, from assessing future flood risk to climate change impacts on food and energy production. These model projections are typically at 12–50-km resolution, providing valuable regional detail but with inherent limitations, in part because of the need to parameterize convection. The first climate change experiments at convection-permitting resolution (kilometer-scale grid spacing) are now available for the United Kingdom; the Alps; Germany; Sydney, Australia; and the western United States. These models give a more realistic representation of convection and are better able to simulate hourly precipitation characteristics that are poorly represented in coarser-resolution climate models. Here we examine these new experiments to determine whether future midlatitude precipitation projections are robust from coarse to higher resolutions, with implications also for the tropics. We find that the explicit representation of the convective storms themselves, only possible in convection-permitting models, is necessary for capturing changes in the intensity and duration of summertime rain on daily and shorter time scales. Other aspects of rainfall change, including changes in seasonal mean precipitation and event occurrence, appear robust across resolutions, and therefore coarse-resolution regional climate models are likely to provide reliable future projections, provided that large-scale changes from the global climate model are reliable. The improved representation of convective storms also has implications for projections of wind, hail, fog, and lightning. We identify a number of impact areas, especially flooding, but also transport and wind energy, for which very high-resolution models may be needed for reliable future assessments.

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Regional Climate Models Add Value to Global Model Data: A Review and Selected Examples

Bulletin of the American Meteorological Society ◽

10.1175/2011bams3061.1 ◽

2011 ◽

Vol 92 (9) ◽

pp. 1181-1192 ◽

Cited By ~ 286

Author(s):

Frauke Feser ◽

Burkhardt Rockel ◽

Hans von Storch ◽

Jörg Winterfeldt ◽

Matthias Zahn

Keyword(s):

Regional Climate Model ◽

Climate Models ◽

Climate Model ◽

Dynamical Downscaling ◽

Spatial Scales ◽

Regional Climate ◽

Regional Climate Models ◽

Small Scale ◽

Model Data ◽

Polar Lows

An important challenge in current climate modeling is to realistically describe small-scale weather statistics, such as topographic precipitation and coastal wind patterns, or regional phenomena like polar lows. Global climate models simulate atmospheric processes with increasingly higher resolutions, but still regional climate models have a lot of advantages. They consume less computation time because of their limited simulation area and thereby allow for higher resolution both in time and space as well as for longer integration times. Regional climate models can be used for dynamical down-scaling purposes because their output data can be processed to produce higher resolved atmospheric fields, allowing the representation of small-scale processes and a more detailed description of physiographic details (such as mountain ranges, coastal zones, and details of soil properties). However, does higher resolution add value when compared to global model results? Most studies implicitly assume that dynamical downscaling leads to output fields that are superior to the driving global data, but little work has been carried out to substantiate these expectations. Here a series of articles is reviewed that evaluate the benefit of dynamical downscaling by explicitly comparing results of global and regional climate model data to the observations. These studies show that the regional climate model generally performs better for the medium spatial scales, but not always for the larger spatial scales. Regional models can add value, but only for certain variables and locations—particularly those influenced by regional specifics, such as coasts, or mesoscale dynamics, such as polar lows. Therefore, the decision of whether a regional climate model simulation is required depends crucially on the scientific question being addressed.

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A Statistical–Dynamical Methodology to Downscale Regional Climate Projections to Urban Scale

Journal of Applied Meteorology and Climatology ◽

10.1175/jamc-d-19-0104.1 ◽

2020 ◽

Vol 59 (6) ◽

pp. 1109-1123 ◽

Cited By ~ 1

Author(s):

François DuchÊne ◽

Bert Van Schaeybroeck ◽

Steven Caluwaerts ◽

Rozemien De Troch ◽

Rafiq Hamdi ◽

...

Keyword(s):

Land Surface ◽

Climate Models ◽

Dynamical Downscaling ◽

Heat Waves ◽

Regional Climate ◽

Wave Number ◽

Surface Model ◽

Climate Projections ◽

Short Period ◽

The Impact

AbstractThe demand of city planners for quantitative information on the impact of climate change on the urban environment is increasing. However, such information is usually extracted from decadelong climate projections generated with global or regional climate models (RCMs). Because of their coarse resolution and unsuitable physical parameterization, however, their model output is not adequate to be used at city scale. A full dynamical downscaling to city level, on the other hand, is computationally too expensive for climatological time scales. A statistical–dynamical computationally inexpensive method is therefore proposed that approximates well the behavior of the full dynamical downscaling approach. The approach downscales RCM simulations using the combination of an RCM at high resolution (H-RES) and a land surface model (LSM). The method involves the setup of a database of urban signatures by running an H-RES RCM with and without urban parameterization for a relatively short period. Using an analog approach, these signatures are first selectively added to the long-term RCM data, which are then used as forcing for an LSM using an urban parameterization in a stand-alone mode. A comparison with a full dynamical downscaling approach is presented for the city of Brussels, Belgium, for 30 summers with the combined ALADIN–AROME model (ALARO-0) coupled to the Surface Externalisée model (SURFEX) as H-RES RCM and SURFEX as LSM. The average bias of the nocturnal urban heat island during heat waves is vanishingly small, and the RMSE is strongly reduced. Not only is the statistical–dynamical approach able to correct the heat-wave number and intensities, it can also improve intervariable correlations and multivariate and temporally correlated indices, such as Humidex.

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