Evaluation of summer precipitation over Far East Asia and South Korea simulated by multiple regional climate models

Park, Changyong; Cha, Dong‐Hyun; Kim, Gayoung; G, Lee; Lee, Dong‐Kyou; Suh, Myoung‐Seok; Hong, Song‐You; Ahn, Joong‐Bae; Min, Seung‐Ki

doi:10.1002/joc.6331

Cited by 16 publications

(7 citation statements)

References 50 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This framework provides consistent high‐resolution climate information for regional impact assessment. The evaluation of CORDEX RCMs and GCMs in simulating precipitation and the comparison of their respective performance have been performed in numerous studies for different parts of the world (e.g., Europe [Prein et al ., 2016; Boé et al ., 2020; Demory et al ., 2020]; South America [Nikulin et al ., 2012; Solman and Blázquez, 2019]; Australia [Di Virgilio et al ., 2020]; East Asia [Lee et al ., 2017; Park et al ., 2019]). The main assumption investigated here and in most of the studies cited above is that RCMs should better simulate precipitation given their ability to capture processes at smaller scales than GCMs.…”

Section: Introductionmentioning

confidence: 99%

More intense daily precipitation in CORDEX‐SEA regional climate models than their forcing global climate models over Southeast Asia

Nguyen

Bador

Alexander

et al. 2022

Intl Journal of Climatology

View full text Add to dashboard Cite

The ability of regional climate models (RCMs) to accurately simulate the current climate is increasingly important for impact assessments over Southeast Asia (SEA), identified as one of the world's most vulnerable regions to climate change. In this study, we evaluate the performance of a set of regional highresolution simulations from the Coordinated Regional Climate Downscaling Experiment-SEA (CORDEX-SEA) in simulating rainfall over the region. Simulations of the 1982-2005 seasonal mean climatology of daily precipitation and precipitation distribution over land are compared to observations from different sources (i.e., in situ-based and satellite-based). We also evaluate to what extent the precipitation distribution in RCMs is closer to observations than their associated forcing global climate models (GCMs). Observational estimates of precipitation over SEA have large uncertainties, making the model evaluations complicated. Despite these difficulties, our results highlight that RCMs can reproduce some complexities in the spatial distribution of seasonal rainfall but generally have a larger wet bias than GCMs. This is particularly true for the extremes in which RCMs show a large overestimation of rainfall intensity. There are some precipitation quantiles and grid points in which RCMs show limited reductions in biases compared to observations, but there is no consistency across all simulations and RCMs are generally further away from observations than their forcing GCMs. We find that greater intensity in RCMs over CORDEX-SEA compared to their associated forcing GCMs is firstly associated with the increased supply of moisture from both local and large-scale sources. Second, a widespread increase in convective precipitation is found across the region in RCMs. Our findings suggest that a model's ability to simulate precipitation over the region relies more on the RCM setup itself (e.g., parameterization scheme), rather than its forcing GCM. This should be considered when assessing the reliability of RCM precipitation simulations for future projections.

show abstract

Section: Introductionmentioning

confidence: 99%

More intense daily precipitation in CORDEX‐SEA regional climate models than their forcing global climate models over Southeast Asia

Nguyen

Bador

Alexander

et al. 2022

Intl Journal of Climatology

View full text Add to dashboard Cite

show abstract

“…Figure 1 shows the spatial location of the sites used. [41]. The climate model outputs' information is summarized in Table 1.…”

Section: Datamentioning

confidence: 99%

Uncertainty of Rate of Change in Korean Future Rainfall Extremes Using Non-Stationary GEV Model

et al. 2021

View full text Add to dashboard Cite

Interest in future rainfall extremes is increasing, but the lack of consistency in the future rainfall extremes outputs simulated in climate models increases the difficulty of establishing climate change adaptation measures for floods. In this study, a methodology is proposed to investigate future rainfall extremes using future surface air temperature (SAT) or dew point temperature (DPT). The non-stationarity of rainfall extremes is reflected through non-stationary frequency analysis using SAT or DPT as a co-variate. Among the parameters of generalized extreme value (GEV) distribution, the scale parameter is applied as a function of co-variate. Future daily rainfall extremes are projected from 16 future SAT and DPT ensembles obtained from two global climate models, four regional climate models, and two representative concentration pathway climate change scenarios. Compared with using only future rainfall data, it turns out that the proposed method using future temperature data can reduce the uncertainty of future rainfall extremes outputs if the value of the reference co-variate is properly set. In addition, the confidence interval of the rate of change of future rainfall extremes is quantified using the posterior distribution of the parameters of the GEV distribution sampled using Bayesian inference.

show abstract

“…Therefore, it is feasible to investigate the future climate using outputs from regional climate models (RCMs), which dynamically downscale the outputs of GCMs. In this study, 16 future (2021-2050) climate ensembles obtained from two GCMs (HadGEM2-AO and MPI_ESM_LR), four RCMs (RegCM4, RSM, MM5 and WRF), and two representative concentration pathway (RCP) climate change scenarios (RCP 4.5 and RCP 8.5) were applied [42].…”

Section: Climate Change Drought Impact Assessmentmentioning

confidence: 99%

Two Ways to Quantify Korean Drought Frequency: Partial Duration Series and Bivariate Exponential Distribution, and Application to Climate Change

et al. 2020

View full text Add to dashboard Cite

Studies using drought index to examine return levels of drought can be classified into two approaches: univariate frequency analysis using annual series extracted from drought index time series and multivariate frequency analysis that simultaneously reflects various characteristics of drought. In the case of drought analysis, it is important to properly consider the duration, so, in this study, univariate frequency analysis is performed using the partial duration series. In addition, a bivariate frequency analysis is performed using a relatively simple bivariate exponential distribution to give a more realistic return level to major drought events in the past while reflecting the correlation between drought severities and durations. The drought severity–duration–frequency curves using each of the two frequency analyses are derived, and these curves are used to examine how the drought phenomenon currently in progress is evolving. From this, the advantages and disadvantages of the two approaches, as well as the points to be aware of in application, are discussed. Finally, using the two approaches to the proposed drought frequency analysis, the behavior of Korea’s future extreme droughts is investigated under the conditions of various future climate change scenarios.

show abstract

Evaluation of summer precipitation over Far East Asia and South Korea simulated by multiple regional climate models

Cited by 16 publications

References 50 publications

More intense daily precipitation in CORDEX‐SEA regional climate models than their forcing global climate models over Southeast Asia

More intense daily precipitation in CORDEX‐SEA regional climate models than their forcing global climate models over Southeast Asia

Uncertainty of Rate of Change in Korean Future Rainfall Extremes Using Non-Stationary GEV Model

Two Ways to Quantify Korean Drought Frequency: Partial Duration Series and Bivariate Exponential Distribution, and Application to Climate Change

Contact Info

Product

Resources

About