Evaluation of multi‐RCM high‐resolution hindcast over the CORDEX East Asia Phase II region: Mean, annual cycle and interannual variations

Yu, Ke; Hui, Pinhong; Zhou, Weiming; Tang, Jianping

doi:10.1002/joc.6323

Cited by 43 publications

(38 citation statements)

References 62 publications

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“…This dataset contains daily average, maximum and minimum temperature and precipitation in the period of 1961-2007. It has been widely used for model verification and evaluation (Yu et al, 2019).…”

Section: Observational Data and Model Datamentioning

confidence: 99%

“…Due to the uncertainty of a single RCM, evident differences exist among different models, which has a great adverse effect on the projection of future climate changes. In order to reduce the uncertainty, multi-GCMs-driven RCM simulations and ensemble methods are adopted to obtain more reliable projections of extreme precipitation changes (Yu et al, 2019;Zhou et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

“…Chen and Gao (2019) found that precipitation will increase in the winter, but mixture of increasing and decreasing in the summer by using one RCM driven by one GCM. Yu et al (2019) used three RCMs driven by ERA-interim Reanalysis data and find that RCMs can only well simulate the seasonal cycle of precipitation in the north area. Han et al (2021) found that the extreme precipitation will be more in the east Asia and the consecutive dry days (CDD) will be enhanced in South China by using 15 setup of 50 km/25 km RCMs under CORDEX East Asia Phase I/II framework.…”

Section: Introductionmentioning

confidence: 99%

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Ensemble Projection of Extreme Precipitation Over China Based on Three Dynamical Downscaling Simulations

Dong¹,

Xie

Fan

et al. 2021

Front. Earth Sci.

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Based on the outputs of the global climate models (GCMs) HadGEM2-ES, NorESM1-M and MPI-ESM-LR from Coupled Model Intercomparison Project Phase 5 (CMIP5) and the downscaling results with the regional climate model (RCM) REMO, the ability of the climate models to reproduce the extreme precipitation in China during the current period (1986–2005) is evaluated. Then, the future extreme precipitation in the mid (2036–2065) and the late 21st century (2066–2095) is projected under the RCP8.5 scenario. The results show that the RCM simulations have great improvements compared with the GCMs, and the ensemble mean of the RCM results (ensR) outperforms each single RCM simulation. The annual precipitation of the RCM simulations is more consistent with the observation than that of the GCMs, with the overestimation of the peak precipitation reduced, and the ensR further reduces the bias. For the extreme precipitation, the RCM simulations significantly decrease the underestimation of intensity in the GCMs. The RCM simulations and the ensR can greatly improve the simulations of Rx5day and CWD compared with the GCMs, decreasing the wet bias in North China and Northwest China. In the future, the consecutive dry days (CDD) will decrease in the northern arid regions, especially in North China and Northeast China. However, the southern regions will experience longer dry period. Both the amount and the intensity of precipitation will increase in various regions of China. The number of wet days will decrease in the south and increase in the north area. The significantly greater Rx5day and R95t indicate more intensive extreme precipitation in the future, and the intensity in the late 21st century will be stronger than that in the middle. Attribution analysis indicates that the extreme precipitation indices especially the R95t have significant positive temporal and spatial correlations with the water vapor flux.

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Section: Observational Data and Model Datamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Ensemble Projection of Extreme Precipitation Over China Based on Three Dynamical Downscaling Simulations

Dong¹,

Xie

Fan

et al. 2021

Front. Earth Sci.

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“…Although the RCM's downscaling skill is strongly limited by the skill of its driving GCM (Racherla et al, 2012), the performance of the two RCMs is dependent on geophysical locations, RCM applied, and extreme indices (Hui et al, 2018a; Yu et al, 2019). In terms of spatial correlation of whole Eastern China, WRF shows advantages in the representation of V95p, R95t, and SDII while PRECIS works better for the number of wet days and CDD.…”

Section: Model Evaluationmentioning

confidence: 99%

“…Dynamic downscaling based on regional climate models (RCMs), which have high spatial resolution, finer surface parameters, and more complicated parameterization schemes, are widely adopted to study regional and local climate and extreme events (Bao et al, 2015; Bürger et al, 2013; Gao et al, 2012; Knutson et al, 2013; Pielke & Wilby, 2012; Singh et al, 2013; Tang et al, 2016; Yu et al, 2019; Zhai et al, 2005; Zou & Zhou et al, 2018; Zhou, 2013). Dynamic downscaling is of great predominance compared to GCM simulations in mean climate and extreme climate event and performs better over East Asia (Gao et al, 2002, 2008, 2012; Hui et al, 2018a; Lee et al, 2014; Park et al, 2016; Wang et al, 2012; Yu et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

Projecting Changes in Mean and Extreme Precipitation Over Eastern China During 2041–2060

Dong

Jiang

Tian

et al. 2020

Earth and Space Science

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This report summarizes the preliminary analysis of the PRECIS 2.0 simulation results, with an emphasis on the priority concerns of Shanghai municipal government and other local governments in the Yangtze River Delta (YRD) Region, and research gaps in the literature. This study employs two regional climate models (RCMs) that are the Providing REgional Climate Impacts for Studies (PRECIS) and Weather Research and Forecasting (WRF), being driven by HadGEM2-ES and IPSL-CM5A, two global circulation models (GCMs) from the Coupled Model Intercomparison Project Phase 5 (CMIP5), to investigate the impact of global warming on the characteristics of mean and extreme precipitation over Eastern China. The capacity of two RCMs and its driving GCMs in reproducing the historical climate during the baseline period (1981-2000) are first evaluated, and then the projections of mean and extreme precipitation over future warming climate period (2041-2060) under the scenarios of the Representative Concentration Pathways (RCPs) 4.5 and 8.5 are carried out. Our analysis shows that with the improved resolution and better representation of finer-scale physical processes, WRF and PRECIS downscaling displays obvious advantages over their driving GCMs (IPSL and HadGEM, respectively) in the validation runs. In particular, the two RCMs are able to capture the observed features of spatial distributions of extreme precipitation indices including V95p, R95t, and SDII. The future projections indicate that increased radiative forcing from RCP4.5 to RCP8.5 emission scenarios would add further strength to the daily precipitation intensity by 2041-2060. Coupled global circulation models (GCMs) forced by the climate scenarios of the Representative Concentration Pathways (RCPs) within the CMIP5 (Coupled Model Intercomparison Project Phase 5) are the state-of-the-art GCMs and the useful tools for estimating trends and variabilities of future climate and extremes, and for evaluating future climate change under various RCP scenarios. Based on the results of CMIP5-GCMs simulations, several studies have presented the changes in extreme precipitation events over different regions under different RCP scenarios (

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Added value of CMIP6 models over CMIP5 models in simulating the climatological precipitation extremes in China

Luo

Guo

Chou

et al. 2021

Intl Journal of Climatology

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Performance of six models in the Coupled Model Intercomparison Project phase 5 (CMIP5) and their new versions in CMIP phase 6 (CMIP6) in representing the climatological (1976–2005) precipitation extremes over China were evaluated based on five precipitation indices. Improvements are found in CMIP6 models in simulating the climatology of all five indices, in which GFDL‐CM4 and GFDL‐ESM4 show significant improvement. Dry biases over South China (SC) are reduced in five CMIP6 models (BCC‐CSM, CanESM, GFDL‐CM, GFDL‐ESM, and IPSL‐CM), with the largest decreased root‐mean‐square error (RMSE) of 59.2% in GFDL‐CM. The reduced dry biases in CMIP6 can be attributed to more moisture transported into SC from the southern boundary and dynamic processes of the atmosphere except in BCC‐CSM, where the increased evaporation dominates. Additionally, increased heavy precipitation events (>20 mm·day−1) are produced over SC in CMIP6 models. Wet biases over West China (WC) are also reduced. with the largest reduced RMSE of 46.8% in GFDL‐CM, which are related to the reduced precipitation frequency (more than 40%) and weakened precipitation intensity. In addition, the CMIP6 models show a higher skill in simulating the frequency distribution of daily precipitation intensity. More heavy precipitation over SC and Northeast China, and fewer weak precipitation (<20 mm·day−1) over WC can be reasonably reproduced. Although the CMIP6 models have obviously improved in simulating total precipitation on wet days, wet days (WD), simple daily intensity index (SDII), and extreme precipitation amount (R95T), the bias still exists in simulating consecutive dry days (CDD).

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Evaluation of multi‐RCM high‐resolution hindcast over the CORDEX East Asia Phase II region: Mean, annual cycle and interannual variations

Cited by 43 publications

References 62 publications

Ensemble Projection of Extreme Precipitation Over China Based on Three Dynamical Downscaling Simulations

Ensemble Projection of Extreme Precipitation Over China Based on Three Dynamical Downscaling Simulations

Projecting Changes in Mean and Extreme Precipitation Over Eastern China During 2041–2060

Added value of CMIP6 models over CMIP5 models in simulating the climatological precipitation extremes in China

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