Present Climate Evaluation and Added Value Analysis of Dynamically Downscaled Simulations of CORDEX—East Asia

Li, Delei; Yin, Baoshu; Feng, Jianlong; Dosio, Alessandro; Geyer, Beate; Qi, Jia; Shi, Hongyuan; Xu, Zhenhua

doi:10.1175/jamc-d-18-0008.1

Cited by 23 publications

(21 citation statements)

References 70 publications

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“…Downscaling techniques, namely, statistical downscaling and dynamical downscaling, have been commonly used to amend the disadvantages of GCMs and to generate region-specific climate information. Dynamical downscaling using regional climate model (RCM) have been used in a wide range of climate applications (Li, Geyer, et al, 2018;Li, von Storch, et al, 2016;Ramesh & Goswami, 2014; Wang et al, 2013). Despite with fine resolution and improvements to the forcing GCM or reanalysis (Feser et al, 2011;Li, 2017), the RCM simulations generally show systematic bias to observations, which is a nonlinear combination of inherited systematic bias from GCM and bias Earth and Space Science 10.1029/2018EA000493 generated by RCMs (Wibig et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

Statistical Bias Correction for Simulated Wind Speeds Over CORDEX‐East Asia

Feng

et al. 2019

Earth and Space Science

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Surface wind is significant for ocean state climate, ocean mixing, and viability of wind energy techniques. However, surface wind simulated from the regional climate model generally features substantial bias from observation. For the first time, this study compares the performance of five bias correction techniques, (1) linear scaling, (2) variance scaling, (3) quantile mapping based on empirical distribution, (4) quantile mapping based on Weibull distribution, and (5) cumulative distribution functions transformation, in reducing the statistical bias of a regional climate model wind output, which was downscaled from a global climate model CNRM‐CM5 during 1991–2000. The surface wind of JRA55 reanalysis data is used as reference. Results show that all bias correction methods are consistent in reducing the climatological mean bias in spatial patterns and intensities. The linear scaling method always performs the worst among all methods in correcting higher‐order statistical biases such as skewness, kurtosis, and wind power density. The other four bias correction methods are generally similar in reducing the statistical biases of different measures based on spatial distribution maps. However, when it comes to spatial averaged mean of statistical measures over CORDEX‐East Asia in January and July, the quantile mapping based on Weibull distribution generally shows the best skills among all methods in bias reduction.

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Section: Introductionmentioning

confidence: 99%

Statistical Bias Correction for Simulated Wind Speeds Over CORDEX‐East Asia

Feng

et al. 2019

Earth and Space Science

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“…RCMs evaluation and configuration have been the subject of a large number of studies, for different regions, such as in Kotlarski et al (2014), Umakanth and Kesarkar (2018), Borge et al (2008), García-Díez et al (2013), Crétat et al (2012), Rajeevan et al (2010), Diro et al (2012), , Reboita et al (2014), Li et al (2018) and Huang et al (2015). For the RCM considered in this study, the COSMO-CLM (Rockel et al, 2008), Europe has received greater attention.…”

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confidence: 99%

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

et al. 2019

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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 twofold: 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 cli-matological 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.

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“…There are 10 grid boxes setting as the sponge zone at each boundary and 40 layers in the vertical direction. The time step is 42 s. The physical parameterizations used in the CCLM are similar to those described in Li et al [46]. Figure 1.…”

Section: Atmospheric Model-cclmmentioning

confidence: 99%

Skill Assessment of an Atmosphere–Wave Regional Coupled Model over the East China Sea with a Focus on Typhoons

Staneva

Grayek

et al. 2020

Atmosphere

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This study performed several sensitivity experiments to investigate the impact of atmosphere–wave coupling on the simulated wind and waves over the East China Sea (ECS) with a focus on typhoon events. These experiments include stand-alone regional atmosphere model (CCLM) simulations, stand-alone spectral wave model (WAM) simulations driven by the regional atmospheric model CCLM or ERA5 reanalysis, and two-way (CCLM-WAM) coupled simulations. We assessed the simulated wind speed and significant wave height against in situ observations and remote sensing data and focused on typhoon events in 2010. We analyzed the differences between the experiments in capturing the surface pressure, wind speed, and roughness length. Both ERA5 reanalysis data and our regional model simulations demonstrate high quality in capturing wind and wave conditions over the ECS. The results show that downscaled simulations tend to be closer to in situ observations than ERA5 reanalysis data in capturing wind variability and probability distribution, dominant wind and wave directions, strong typhoon intensity and related extreme significant wave height. In comparison with satellite observations, the CCLM-WAM simulation outperforms the CCLM in reducing wind bias. The coupled and uncoupled simulations are very similar in terms of other wind and wave statistics. Though there is much improvement in capturing typhoon intensity to ERA5, regional downscaled simulations still underestimate the wind intensity of tropical cyclones.

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Present Climate Evaluation and Added Value Analysis of Dynamically Downscaled Simulations of CORDEX—East Asia

Cited by 23 publications

References 70 publications

Statistical Bias Correction for Simulated Wind Speeds Over CORDEX‐East Asia

Statistical Bias Correction for Simulated Wind Speeds Over CORDEX‐East Asia

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

Skill Assessment of an Atmosphere–Wave Regional Coupled Model over the East China Sea with a Focus on Typhoons

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