scholarly journals A new spatially distributed added value index for regional climate models: the EURO-CORDEX and the CORDEX-CORE highest resolution ensembles

2020 ◽  
Author(s):  
James M. Ciarlo` ◽  
Erika Coppola ◽  
Adriano Fantini ◽  
Filippo Giorgi ◽  
XueJie Gao ◽  
...  
Keyword(s):  
Climate Models ◽  
Regional Climate ◽  
Regional Climate Models ◽  
Added Value ◽  
Spatially Distributed

Applying the new spatially distributed Added Value Index and Climate Change Downscaling Signal for Regional Climate Models to high-resolution EURO-CORDEX and convection permitting scale simulations

2021 ◽  
Author(s):  
James Ciarlo ◽  
Erika Coppola ◽  
Emanuela Pichelli ◽  
Jose Abraham Torres Alavez ◽  
Keyword(s):  
Climate Change ◽  
High Resolution ◽  
General Circulation ◽  
Climate Models ◽  
Daily Precipitation ◽  
Regional Climate ◽  
Regional Climate Models ◽  
General Circulation Models ◽  
Added Value ◽  
Spatially Distributed

<p>Downscaling data from General Circulation Models (GCMs) with Regional Climate Models (RCMs) is a computationally expensive process, even more so running at the convection permitting scale (CP). Despite the high-resolution products of these simulations, the Added Value (AV) of these runs compared to their driving models is an important factor for consideration. A new method was recently developed to quantify the AV of historical simulations as well as the Climate Change Downscaling Signal (CCDS) of forecast runs. This method presents these quantities spatially and thus the specific regions with the most AV can be identified and understood.</p><p>An analysis of daily precipitation from a 55-model EURO-CORDEX ensemble (at 12 km resolution) was assessed using this method. It revealed positive AV throughout the domain with greater emphasis in regions of complex topography, coast-lines, and the tropics. Similar CCDS was obtained when assessing the RCP 8.5 far future runs in these domains. This paper looks more closely at the CCDS obtained with this method and compares it to other climate change signals described in other studies.</p><p>The same method is now being applied to assess the AV and CCDS of daily precipitation from an ensemble of models at the CP scale (~3 km) over different domains within Europe. The current stage of the analysis is also looking into the AV of using hourly precipitation instead of daily.</p>

scholarly journals A new spatially distributed Added Value Index for Regional Climate Models: the EURO-CORDEX and the CORDEX-CORE highest resolution ensembles

2020 ◽  
Author(s):  
James M. Ciarlo` ◽  
Erika Coppola ◽  
Adriano Fantini ◽  
XueJie Gao ◽  
Yao Tong ◽  
...  
Keyword(s):  
General Circulation ◽  
Climate Models ◽  
Regional Climate ◽  
Regional Climate Models ◽  
General Circulation Models ◽  
New Method ◽  
Added Value ◽  
Tropical Regions ◽  
Spatially Distributed ◽  
Rcp 8.5

<p>Regional Climate Models (RCMs) have undergone substantial development, resulting in increasingly reliable high-resolution simulations. Despite this, the added value of these simulations compared to their driving General Circulation Models (GCMs) has been a recurring issue. Past studies have used different techniques to quantify the added value of a RCM. A new method is now being presented, based on these past studies, that quantifies the added value and presents it spatially. The method was also adapted to assess the Downscaling Signal (DS) in climate change simulations and compare this to the added value.</p><p>This new method has been used to assess the daily precipitation of the 55-model EURO-CORDEX ensemble and the CORDEX-CORE ensemble, focusing especially on the higher-end of the PDFs. This revealed an overall positive added value across all domains, especially in areas of complex topography, cost-lines, and tropical regions. This DS was similar to that of the added value when looking at RCP 8.5 far-future simulations.</p>

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

Atmosphere ◽  
2018 ◽  
Vol 9 (7) ◽  
pp. 262 ◽  
Author(s):  
Coraline Wyard ◽  
Sébastien Doutreloup ◽  
Alexandre Belleflamme ◽  
Martin Wild ◽  
Xavier Fettweis
Keyword(s):  
Cloud Cover ◽  
Climate Models ◽  
Climate Model ◽  
Regional Climate ◽  
Regional Climate Models ◽  
Horizontal Resolution ◽  
Radiative Flux ◽  
Added Value ◽  
Historical Trends ◽  
The Mean

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.

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

2019 ◽  
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.

scholarly journals Assessment of Bias Assumptions for Climate Models

2014 ◽  
Vol 27 (17) ◽  
pp. 6799-6818 ◽  
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.

scholarly journals Stochastic Rainfall Downscaling of Climate Models

2014 ◽  
Vol 15 (2) ◽  
pp. 830-843 ◽  
Author(s):  
D. D’Onofrio ◽  
E. Palazzi ◽  
J. von Hardenberg ◽  
A. Provenzale ◽  
S. Calmanti
Keyword(s):  
Climate Models ◽  
Global Climate ◽  
Regional Climate ◽  
Regional Climate Models ◽  
Amplitude Distribution ◽  
Global Climate Models ◽  
Added Value ◽  
Small Scale ◽  
Physically Based ◽  
Stochastic Downscaling

Abstract Precipitation extremes and small-scale variability are essential drivers in many climate change impact studies. However, the spatial resolution currently achieved by global climate models (GCMs) and regional climate models (RCMs) is still insufficient to correctly identify the fine structure of precipitation intensity fields. In the absence of a proper physically based representation, this scale gap can be at least temporarily bridged by adopting a stochastic rainfall downscaling technique. In this work, a precipitation downscaling chain is introduced where the global 40-yr ECMWF Re-Analysis (ERA-40) (at about 120-km resolution) is dynamically downscaled using the Protheus RCM at 30-km resolution. The RCM precipitation is then further downscaled using a stochastic downscaling technique, the Rainfall Filtered Autoregressive Model (RainFARM), which has been extended for application to long climate simulations. The application of the stochastic downscaling technique directly to the larger-scale reanalysis field at about 120-km resolution is also discussed. To assess the ability of this approach in reproducing the main statistical properties of precipitation, the downscaled model results are compared with the precipitation data provided by a dense network of 122 rain gauges in northwestern Italy, in the time period from 1958 to 2001. The high-resolution precipitation fields obtained by stochastically downscaling the RCM outputs reproduce well the seasonality and amplitude distribution of the observed precipitation during most of the year, including extreme events and variance. In addition, the RainFARM outputs compare more favorably to observations when the procedure is applied to the RCM output rather than to the global reanalyses, highlighting the added value of reaching high enough resolution with a dynamical model.

scholarly journals Dynamical Downscaling in Seasonal Climate Forecasts: Comparison between RegCM- and WRF-Based Approaches

Atmosphere ◽  
2021 ◽  
Vol 12 (6) ◽  
pp. 757
Author(s):  
Lorenzo Sangelantoni ◽  
Antonio Ricchi ◽  
Rossella Ferretti ◽  
Gianluca Redaelli
Keyword(s):  
Large Scale ◽  
Climate Models ◽  
Dynamical Downscaling ◽  
Regional Climate ◽  
Regional Scale ◽  
Central Italy ◽  
Probabilistic Approach ◽  
Regional Climate Models ◽  
Added Value ◽  
Temperature Fields

The purpose of the present study is to assess the large-scale signal modulation produced by two dynamically downscaled Seasonal Forecasting Systems (SFSs) and investigate if additional predictive skill can be achieved, compared to the driving global-scale Climate Forecast System (CFS). The two downscaled SFSs are evaluated and compared in terms of physical values and anomaly interannual variability. Downscaled SFSs consist of two two-step dynamical downscaled ensembles of NCEP-CFSv2 re-forecasts. In the first step, the CFS field is downscaled from 100 km to 60 km over Southern Europe (D01). The second downscaling, driven by the corresponding D01, is performed at 12 km over Central Italy (D02). Downscaling is performed using two different Regional Climate Models (RCMs): RegCM v.4 and WRF 3.9.1.1. SFS skills are assessed over a period of 21 winter seasons (1982–2002), by means of deterministic and probabilistic approach and with a metric specifically designed to isolate downscaling signal over different percentiles of distribution. Considering the temperature fields and both deterministic and probabilistic metrics, regional-scale SFSs consistently improve the original CFS Seasonal Anomaly Signal (SAS). For the precipitation, the added value of downscaled SFSs is mainly limited to the topography driven refinement of precipitation field, whereas the SAS is mainly “inherited” by the driving CFS. The regional-scale SFSs do not seem to benefit from the second downscaling (D01 to D02) in terms of SAS improvement. Finally, WRF and RegCM show substantial differences in both SAS and climatologically averaged fields, highlighting a different impact of the common SST driving field.

Added Value of reproduced precipitation by high resolved regional climate model simulation over CORDEX-East Asia

2020 ◽  
Author(s):  
Taehyung Kim ◽  
Dong-Hyun Cha ◽  
Gayoung Kim ◽  
Seok-Woo Shin ◽  
Changyong Park ◽  
...  
Keyword(s):  
East Asia ◽  
Extreme Precipitation ◽  
Climate Models ◽  
Phase 1 ◽  
Regional Climate ◽  
Regional Climate Models ◽  
Added Value ◽  
Phase 2 ◽  
Fine Resolution ◽  
Cordex East Asia

<p>In the framework of the CORDEX-East Asia, evaluation simulations using high-resolution regional climate models (SNURCM and HadGEM3-RA) with ~25km (Phase2) grid scale have been conducted. In this study, we investigate whether the higher-resolution regional climate models (RCMs) can generate added values for summer mean precipitation, large-scale circulation, and extreme precipitation compared to those with lower-resolution (~50km, Phase 1). In addition, the added value index is used to quantitatively analyze the abilities of fine- and coarse-resolution RCMs. Hence, sets of phase 1 and phase 2 simulations of two RCMs are compared to observations in the East Asia region. In SNURCM simulations, positive (negative) added value of summer mean precipitation is reproduced over most ocean (land) region of East Asia in fine-resolution simulation. Extreme precipitation over Korea and Japan is well reproduced in Phase 2 simulations because the simulations of typhoons and East Asia summer monsoon are improved. In HadGEM3-RA simulations, the results of summer mean precipitation over most East Asian regions above 25°N are improved in Phase 2, while worse results are reproduced below 25°N. But, extreme precipitation in fine-resolution simulation is adequately reproduced in most regions of East Asia except China and the Yellow sea. As a result, the results of the simulations are different depending on the characteristics of the individual models, but more positive added values for the intensity and spatial distribution of precipitation over East Asia are generated as the horizontal resolution of RCMs increases.</p><p>This work was funded by the Korea Meteorological Administration Research and Development Program under Grant KMI(KMI2018-01211)</p><p> </p>

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