Global Radiative Flux and Cloudiness Variability for the Period 1959–2010 in Belgium: A Comparison between Reanalyses and the Regional Climate Model MAR

The use of regional climate models (RCMs) can partly reduce the biases in global radiative flux (Eg↓) that are found in reanalysis products and global models, as they allow for a finer spatial resolution and a finer parametrisation of surface and atmospheric processes. In this study, we assess the ability of the MAR («Modèle Atmosphérique Régional») RCM to reproduce observed changes in Eg↓, and we investigate the added value of MAR with respect to reanalyses. Simulations were performed at a horizontal resolution of 5 km for the period 1959–2010 by forcing MAR with different reanalysis products: ERA40/ERA-interim, NCEP/NCAR-v1, ERA-20C, and 20CRV2C. Measurements of Eg↓ from the Global Energy Balance Archive (GEBA) and from the Royal Meteorological Institute of Belgium (RMIB), as well as cloud cover observations from Belgocontrol and RMIB, were used for the evaluation of the MAR model and the forcing reanalyses. Results show that MAR enables largely reducing the mean biases that are present in the reanalyses. The trend analysis shows that only MAR forced by ERA40/ERA-interim shows historical trends, which is probably because the ERA40/ERA-interim has a better horizontal resolution and assimilates more observations than the other reanalyses that are used in this study. The results suggest that the solar brightening observed since the 1980s in Belgium has mainly been due to decreasing cloud cover.

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The impact of RCM formulation and resolution on simulated precipitation in Africa

10.5194/esd-2019-55 ◽

2019 ◽

Cited By ~ 2

Author(s):

Minchao Wu ◽

Grigory Nikulin ◽

Erik Kjellström ◽

Danijel Belušić ◽

Colin Jones ◽

...

Keyword(s):

Climate Models ◽

Regional Climate ◽

Regional Climate Models ◽

Horizontal Resolution ◽

Added Value ◽

Primary Control ◽

Model Formulation ◽

Simulated Precipitation ◽

Mean Climate ◽

The Impact

Abstract. We investigate the impact of model formulation and horizontal resolution on the ability of Regional Climate Models (RCMs) to simulate precipitation in Africa. Two RCMs – SMHI-RCA4 and HCLIM38-ALADIN are utilized for downscaling the ERA-Interim reanalysis over Africa at four different resolutions: 25, 50, 100 and 200 km. Additionally to the two RCMs, two different configurations of the same RCA4 are used. Contrasting different RCMs, configurations and resolutions it is found that model formulation has the primary control over many aspects of the precipitation climatology in Africa. Patterns of spatial biases in seasonal mean precipitation are mostly defined by model formulation while the magnitude of the biases is controlled by resolution. In a similar way, the phase of the diurnal cycle is completely controlled by model formulation (convection scheme) while its amplitude is a function of resolution. Although higher resolution in many cases leads to smaller biases in the time mean climate, the impact of higher resolution is mixed. An improvement in one region/season (e.g. reduction of dry biases) often corresponds to a deterioration in another region/season (e.g. amplification of wet biases). The experiments confirm a pronounced and well known impact of higher resolution – a more realistic distribution of daily precipitation. Even if the time-mean climate is not always greatly sensitive to resolution, what the time-mean climate is made up of, higher order statistics, is sensitive. Therefore, the realism of the simulated precipitation increases as resolution increases. Our results show that improvements in the ability of RCMs to simulate precipitation in Africa compared to their driving reanalysis in many cases are simply related to model formulation and not necessarily to higher resolution. Such model formulation related improvements are strongly model dependent and in general cannot be considered as an added value of downscaling.

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Assessment of Bias Assumptions for Climate Models

Journal of Climate ◽

10.1175/jcli-d-13-00716.1 ◽

2014 ◽

Vol 27 (17) ◽

pp. 6799-6818 ◽

Cited By ~ 24

Author(s):

Christian Kerkhoff ◽

Hans R. Künsch ◽

Christoph Schär

Keyword(s):

Surface Temperature ◽

Climate Models ◽

Climate Model ◽

Spatial Scales ◽

Regional Climate ◽

Lower Troposphere ◽

Regional Climate Models ◽

Added Value ◽

Model Biases ◽

Interaction Component

Abstract Climate scenarios make implicit or explicit assumptions about the extrapolation of climate model biases from current to future time periods. Such assumptions are inevitable because of the lack of future observations. This manuscript reviews different bias assumptions found in the literature and provides measures to assess their validity. The authors explicitly separate climate change from multidecadal variability to systematically analyze climate model biases in seasonal and regional surface temperature averages, using global and regional climate models (GCMs and RCMs) from the Ensemble-Based Predictions of Climate Changes and Their Impacts (ENSEMBLES) project over Europe. For centennial time scales, it is found that a linear bias extrapolation for GCMs is best supported by the analysis: that is, it is generally not correct to assume that model biases are independent of the climate state. Results also show that RCMs behave markedly differently when forced with different drivers. RCM and GCM biases are not additive, and there is a significant interaction component in the bias of the RCM–GCM model chain that depends on both the RCM and GCM considered. This result questions previous studies that deduce biases (and ultimately projections) in RCM–GCM combinations from reanalysis-driven simulations. The authors suggest that the aforementioned interaction component derives from the refined RCM representation of dynamical and physical processes in the lower troposphere, which may nonlinearly depend upon the larger-scale circulation stemming from the driving GCM. The authors’ analyses also show that RCMs provide added value and that the combined RCM–GCM approach yields, in general, smaller biases in seasonal surface temperature and interannual variability, particularly in summer and even for spatial scales that are, in principle, well resolved by the GCMs.

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Evaluation and Bias Correction of Regional Climate Model Results Using Model Evaluation Measures

Journal of Applied Meteorology and Climatology ◽

10.1175/jamc-d-11-0161.1 ◽

2012 ◽

Vol 51 (9) ◽

pp. 1670-1684 ◽

Cited By ~ 36

Author(s):

Robert Schoetter ◽

Peter Hoffmann ◽

Diana Rechid ◽

K. Heinke Schlünzen

Keyword(s):

Wind Speed ◽

Bias Correction ◽

Model Evaluation ◽

Cloud Cover ◽

Climate Models ◽

Climate Model ◽

Regional Climate ◽

Regional Climate Models ◽

Total Cloud Cover ◽

Evaluation Measures

AbstractFor the assessment of regional climate change the reliability of the regional climate models needs to be known. The main goal of this paper is to evaluate the quality of climate model data that are used for impact research. Temperature, precipitation, total cloud cover, relative humidity, and wind speed simulated by the regional climate models Climate Local Model (CLM) and Regional Model (REMO) are evaluated for the metropolitan region of Hamburg in northern Germany for the period 1961–2000. The same evaluation is performed for the global climate model ECHAM5 that is used to force the regional climate models. The evaluation is based on comparison of the simulated and observed climatological annual cycles and probability density functions of daily averages. Several model evaluation measures are calculated to assure an objective model evaluation. As a very selective model evaluation measure, the hit rate of the percentiles is introduced for the evaluation of daily averages. The influence of interannual climate variability is considered by determining confidence intervals for the model evaluation measures by bootstrap resampling. Evaluation shows that, with some exceptions, temperature and wind speed are well simulated by the climate models; whereas considerable biases are found for relative humidity, total cloud cover, and precipitation, although not for all models in all seasons. It is shown that model evaluation measures can be used to decide for which meteorological parameters a bias correction is reasonable.

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Regional climate change projections for the Barents region

10.5194/esd-2016-27 ◽

2016 ◽

Author(s):

Andreas Dobler ◽

Jan Erik Haugen ◽

Rasmus Emil Benestad

Keyword(s):

Sea Ice ◽

Regional Climate Model ◽

Cloud Cover ◽

Climate Models ◽

Climate Model ◽

Barents Sea ◽

Regional Climate ◽

Regional Climate Models ◽

Barents Region ◽

The Barents Sea

Abstract. Regional climate models can provide estimates for quantities that are difficult to study in empirical studies, such as cloud cover, wind, sea-ice or dependencies between variables. In this study, the regional climate model COSMO-CLM was used to simulate local climate conditions over the Barents region and provide projections for the three emission scenarios RCP2.6, RCP4.5 and RCP8.5. The results indicate that the most pronounced local warming can be expected in winter in the high Arctic near the present sea-ice border. The changes reach up to 20K, resulting in future temperatures close to melting. Similar spatial patterns are seen for changes in precipitation and wind in all scenarios, but with different amplitudes. Precipitation sensitivities, however, show the highest values along the west coast of Norway and in the Arctic during summer. For clouds, the projections show a decrease in winter mean cloud cover over sea and an increase over land, dominated by changes in low layer clouds. Over the Barents sea, convective cloud fraction is projected to increase, together with an increases in convective and total precipitation. In contrast to the COSMO-CLM and two other regional climate models taken into account, the ensemble mean of the driving global models shows an increasing trend in total cloud cover over the Barents sea. An analysis of the opposing trends reveals that there is an added value in the regional climate model projections for the Barents region.

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Added value of the EURO-CORDEX high-resolution downscaling over the Iberian Peninsula revisited. Part II: Max and Min Temperature

10.5194/gmd-2021-208 ◽

2021 ◽

Author(s):

João António Martins Careto ◽

Pedro Miguel Matos Soares ◽

Rita Margarida Cardoso ◽

Sixto Herrera ◽

José Manuel Gutiérrez

Keyword(s):

Iberian Peninsula ◽

Minimum Temperature ◽

Climate Models ◽

Regional Climate ◽

Regional Climate Models ◽

Horizontal Resolution ◽

Added Value ◽

Maximum Temperature ◽

European Continent ◽

Partial Probability

Abstract. In the recent past, the increase of computation resources led to the appearance of regional climate models with increasing domains and resolutions, spamming larger temporal periods. A good example is the World Climate Research Program – Coordinated Regional Climate Downscaling Experiment for the European domain (EURO-CORDEX). This set of regional models encompass the entire European continent, for a 130-year common period until the end of the 21st century, while having a 12 Km horizontal resolution. Such simulations are computationally demanding, while at the same time, not always showing added value. This study considers a recently proposed metric in order to assess the added value of the EURO-CORDEX Hindcast (1989–2008) and Historical (1971–2005) simulations, for the maximum and minimum temperature over the Iberian Peninsula. This approach allows an evaluation of the higher against the driving lower resolutions relative to the performance of the whole or partial probability density functions, by having an observational regular gridded dataset as reference. Overall, the gains for maximum temperature are more relevant in comparison to minimum temperature, partially owed to known problems derived from the snow-albedo-atmosphere feedback. For more local scales, areas near the coast reveal significant added value in comparison with the interior, which displays limited gains and sometimes significant detrimental effects around −30 %. Nevertheless, the added value for temperature extremes reveals a similar range, although with stronger gains in coastal regions and in locations from the interior for maximum temperature, contrasting with the losses for locations in the interior of the domain for the minimum temperature.

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The CORDEX-CORE EXP-I initiative: Description and highlight results from the initial analysis

Bulletin of the American Meteorological Society ◽

10.1175/bams-d-21-0119.1 ◽

2021 ◽

pp. 1-52

Author(s):

Filippo Giorgi ◽

Erika Coppola ◽

Daniela Jacob ◽

Claas Teichmann ◽

Sabina Abba Omar ◽

...

Keyword(s):

Climate Models ◽

Climate Model ◽

Global Climate ◽

Global Climate Model ◽

Regional Climate ◽

Regional Climate Change ◽

Regional Climate Models ◽

Added Value ◽

Initial Analysis ◽

Wide Range

AbstractWe describe the first effort within the Coordinated Regional Climate Downscaling Experiment - Coordinated Output for Regional Evaluation, or CORDEX-CORE EXP-I. It consists of a set of 21st century projections with two regional climate models (RCMs) downscaling three global climate model (GCM) simulations from the CMIP5 program, for two greenhouse gas concentration pathways (RCP8.5 and RCP2.6), over 9 CORDEX domains at ~25 km grid spacing. Illustrative examples from the initial analysis of this ensemble are presented, covering a wide range of topics, such as added value of RCM nesting, extreme indices, tropical and extratropical storms, monsoons, ENSO, severe storm environments, emergence of change signals, energy production. They show that the CORDEX-CORE EXP-I ensemble can provide downscaled information of unprecedented comprehensiveness to increase understanding of processes relevant for regional climate change and impacts, and to assess the added value of RCMs. The CORDEX-CORE EXP-I dataset, which will be incrementally augmented with new simulations, is intended to be a public resource available to the scientific and end-user communities for application to process studies, impacts on different socioeconomic sectors and climate service activities. The future of the CORDEX-CORE initiative is also discussed.

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The impact of regional climate model formulation and resolution on simulated precipitation in Africa

Earth System Dynamics ◽

10.5194/esd-11-377-2020 ◽

2020 ◽

Vol 11 (2) ◽

pp. 377-394 ◽

Cited By ~ 2

Author(s):

Minchao Wu ◽

Grigory Nikulin ◽

Erik Kjellström ◽

Danijel Belušić ◽

Colin Jones ◽

...

Keyword(s):

Climate Models ◽

Climate Model ◽

Regional Climate ◽

Horizontal Resolution ◽

Added Value ◽

Primary Control ◽

Model Formulation ◽

Simulated Precipitation ◽

Mean Climate ◽

The Impact

Abstract. We investigate the impact of model formulation and horizontal resolution on the ability of Regional Climate Models (RCMs) to simulate precipitation in Africa. Two RCMs (SMHI-RCA4 and HCLIM38-ALADIN) are utilized for downscaling the ERA-Interim reanalysis over Africa at four different resolutions: 25, 50, 100, and 200 km. In addition to the two RCMs, two different parameter settings (configurations) of the same RCA4 are used. By contrasting different downscaling experiments, it is found that model formulation has the primary control over many aspects of the precipitation climatology in Africa. Patterns of spatial biases in seasonal mean precipitation are mostly defined by model formulation, while the magnitude of the biases is controlled by resolution. In a similar way, the phase of the diurnal cycle in precipitation is completely controlled by model formulation (convection scheme), while its amplitude is a function of resolution. However, the impact of higher resolution on the time-mean climate is mixed. An improvement in one region/season (e.g. reduction in dry biases) often corresponds to a deterioration in another region/season (e.g. amplification of wet biases). At the same time, higher resolution leads to a more realistic distribution of daily precipitation. Consequently, even if the time-mean climate is not always greatly sensitive to resolution, the realism of the simulated precipitation increases as resolution increases. Our results show that improvements in the ability of RCMs to simulate precipitation in Africa compared to their driving reanalysis in many cases are simply related to model formulation and not necessarily to higher resolution. Such model formulation related improvements are strongly model dependent and can, in general, not be considered as an added value of downscaling.

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First results of a comparison study of multi-domain REMO CORDEX simulations between 0.11° and 0.22° resolution with ERA-Interim forcing

10.5194/egusphere-egu2020-20151 ◽

2020 ◽

Author(s):

Martina Schubert-Frisius ◽

Susanne Pfeifer ◽

Armelle Reca Remedio ◽

Claas Teichmann ◽

Lars Buntemeyer ◽

...

Keyword(s):

Spatial Resolution ◽

Climate Models ◽

Climate Model ◽

Regional Climate ◽

Virtual Water ◽

Regional Climate Models ◽

Added Value ◽

The Arctic ◽

Climate Projections ◽

First Results

Within the framework of WCRP CORDEX, the CORE (CORDEX Coordinated Output for Regional Evaluations) experiment provides a homogeneous ensemble of regional climate projections for 9 domains covering all land areas of the globe with the exception of the Arctic and Antarctic regions (http://www.cordex.org/experiment-guidelines/cordex-core/). CORDEX-CORE provides data from two regional climate models (REMO2015 and RegCM), driven by 3 GCMs and under 2 RCP scenario conditions at a resolution of about 25 km. In addition, within the same framework, simulations of the current climate, driven by ERA-Interim, were carried out for all areas with REMO2015 at a grid resolution of approx. 25 km.Within the German Project ViWA (Virtual Water Values, https://viwa.geographie-muenchen.de), simulations with the regional climate model REMO2015, driven by ERA-INTERIM analyses were carried out for the same regions globally, but on a significantly higher spatial resolution of approx. 12.5 km. These simulations cover the time period from 2015 to 2018. Comparing these highly resolved simulations to the coarsely resolved CORDEX-CORE simulations, can give indications, in which regions and for which processes the CORDEX-CORE resolution of 25 km is sufficient and where a higher resolution brings a clear added value.We will show first results of this comparison, focusing on selected regions and processes which potentially benefit from higher spatial resolution of the simulations.

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Projecting Health Impacts of Future Temperature: A Comparison of Quantile-Mapping Bias-Correction Methods

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph18041992 ◽

2021 ◽

Vol 18 (4) ◽

pp. 1992

Author(s):

Weijia Qian ◽

Howard H. Chang

Keyword(s):

Bias Correction ◽

Climate Models ◽

Climate Model ◽

Regional Climate ◽

Warm Season ◽

Climate Science ◽

Regional Climate Models ◽

Health Impact ◽

Quantile Mapping ◽

Ed Visits

Health impact assessments of future environmental exposures are routinely conducted to quantify population burdens associated with the changing climate. It is well-recognized that simulations from climate models need to be bias-corrected against observations to estimate future exposures. Quantile mapping (QM) is a technique that has gained popularity in climate science because of its focus on bias-correcting the entire exposure distribution. Even though improved bias-correction at the extreme tails of exposure may be particularly important for estimating health burdens, the application of QM in health impact projection has been limited. In this paper we describe and apply five QM methods to estimate excess emergency department (ED) visits due to projected changes in warm-season minimum temperature in Atlanta, USA. We utilized temperature projections from an ensemble of regional climate models in the North American-Coordinated Regional Climate Downscaling Experiment (NA-CORDEX). Across QM methods, we estimated consistent increase in ED visits across climate model ensemble under RCP 8.5 during the period 2050 to 2099. We found that QM methods can significantly reduce between-model variation in health impact projections (50–70% decreases in between-model standard deviation). Particularly, the quantile delta mapping approach had the largest reduction and is recommended also because of its ability to preserve model-projected absolute temporal changes in quantiles.

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Regional Climate Model Inter-Comparison for Antarctica within a Data Science Framework

10.5194/egusphere-egu21-4892 ◽

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

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.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.

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