Intercomparison of projected changes in climate extremes for South Korea: application of trend preserving statistical downscaling methods to the <scp>CMIP5</scp> ensemble

Eum, Hyung‐Il; Cannon, Alex J.

doi:10.1002/joc.4924

Cited by 86 publications

(85 citation statements)

References 60 publications

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“…Quantile mapping is a powerful bias correction method for adjusting not only mean but also variance and extreme distributions (Chen et al, 2013;Eum & Cannon, 2017;Teutschbein & Seibert, 2012). To overcome this defect, QDM has been developed to conserve projected relative changes in quantiles (Bhatia et al, 2019;Cannon et al, 2015;Eum & Cannon, 2017). To overcome this defect, QDM has been developed to conserve projected relative changes in quantiles (Bhatia et al, 2019;Cannon et al, 2015;Eum & Cannon, 2017).…”

Section: Methodssupporting

confidence: 89%

“…Table S2, the K-T classification uses raw temperature and precipitation rather than anomaly when defining thresholds. Quantile mapping is a powerful bias correction method for adjusting not only mean but also variance and extreme distributions (Chen et al, 2013;Eum & Cannon, 2017;Teutschbein & Seibert, 2012). Quantile mapping is a powerful bias correction method for adjusting not only mean but also variance and extreme distributions (Chen et al, 2013;Eum & Cannon, 2017;Teutschbein & Seibert, 2012).…”

Section: Methodsmentioning

confidence: 99%

“…Therefore, the results are crucially affected by the model biases, and so we applied a bias correction method, quantile delta mapping (QDM), based on cumulative distribution function matching. Quantile mapping is a powerful bias correction method for adjusting not only mean but also variance and extreme distributions (Chen et al, 2013;Eum & Cannon, 2017;Teutschbein & Seibert, 2012). However, it has disadvantages when applied to the future climate change data because of the stationary assumption that artificially offsets the model-projected trends (Hagemann et al, 2011;Maraun et al, 2017;Maurer & Pierce, 2014).…”

Section: Methodsmentioning

confidence: 99%

“…However, it has disadvantages when applied to the future climate change data because of the stationary assumption that artificially offsets the model-projected trends (Hagemann et al, 2011;Maraun et al, 2017;Maurer & Pierce, 2014). To overcome this defect, QDM has been developed to conserve projected relative changes in quantiles (Bhatia et al, 2019;Cannon et al, 2015;Eum & Cannon, 2017). A detailed description of QDM is available in Appendix A of Cannon et al (2015).…”

Section: Methodsmentioning

confidence: 99%

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The Köppen‐Trewartha Climate‐Type Changes Over the CORDEX‐East Asia Phase 2 Domain Under 2 and 3 °C Global Warming

Ahn

Cha

et al. 2019

Geophysical Research Letters

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For the comprehensive estimation of regional climate change over East Asia (EA) at the 2 and 3°C global warming levels (GWLs), the Köppen-Trewartha climate-type change is assessed with ensemble regional climate change projections in line with Coordinated Regional Climate Downscaling Experiment (CORDEX)-EA phase 2. Under the 2°C (3°C) GWL, 17.6% (25.2%) of the EA region is expected to undergo major climate-type changes. Tropical and subtropical climate types will expand northward, accompanied by increasing hydroclimatic intensity. Limiting GWL to 2°C shows benefits by preventing subtropical-type expansion around far EA. Desertification over inland regions of EA exhibits scenario dependency. Boreal and tundra climate types over high-latitude regions will tend to decrease rapidly, especially over the Tibetan Plateau. The results are expected to be a baseline assessment of climate change over EA under the 2 and 3°C GWLs above the preindustrial level.Plain Language Summary This paper investigates the Köppen-Trewartha climate-type changes over East Asia (EA) at the 2 and 3°C global warming levels (GWLs) with the ensemble regional climate simulations. Under the 2°C (3°C) GWL, 17.6% (25.2%) of the EA region is expected to undergo major climate-type changes. The result shows increased tropical, subtropical, and desert climate types accompanied by intensification of hydroclimatic stress. In addition, boreal and tundra climate types over high-latitude regions will tend to decrease rapidly, especially over the Tibetan Plateau under the 2 and 3°C GWLs.

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Section: Methodssupporting

confidence: 89%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

See 2 more Smart Citations

The Köppen‐Trewartha Climate‐Type Changes Over the CORDEX‐East Asia Phase 2 Domain Under 2 and 3 °C Global Warming

Ahn

Cha

et al. 2019

Geophysical Research Letters

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“…In a bias correction of precipitation data, the quantile mapping (QM), the detrended quantile mapping (DQM), and the quantile delta mapping (QDM) methods have been widely employed because they can correct biases considering high order moment [12]. Additionally, these methods were designed to preserve long-term changes in quantiles projected by climate models [12,29]. Thus, the QM-based bias correction method is employed in this study.…”

Section: Quantile Mapping Based Bias Correction Methodsmentioning

confidence: 99%

Probability Distributions for a Quantile Mapping Technique for a Bias Correction of Precipitation Data: A Case Study to Precipitation Data Under Climate Change

Heo

Ahn

Shin

et al. 2019

Water

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The quantile mapping method is a bias correction method that leads to a good performance in terms of precipitation. Selecting an appropriate probability distribution model is essential for the successful implementation of quantile mapping. Probability distribution models with two shape parameters have proved that they are fit for precipitation modeling because of their flexibility. Hence, the application of a two-shape parameter distribution will improve the performance of the quantile mapping method in the bias correction of precipitation data. In this study, the applicability and appropriateness of two-shape parameter distribution models are examined in quantile mapping, for a bias correction of simulated precipitation data from a climate model under a climate change scenario. Additionally, the impacts of distribution selection on the frequency analysis of future extreme precipitation from climate are investigated. Generalized Lindley, Burr XII, and Kappa distributions are used, and their fits and appropriateness are compared to those of conventional distributions in a case study. Applications of two-shape parameter distributions do lead to better performances in reproducing the statistical characteristics of observed precipitation, compared to those of conventional distributions. The Kappa distribution is considered the best distribution model, as it can reproduce reliable spatial dependences of the quantile corresponding to a 100-year return period, unlike the gamma distribution.Eden et al. [6] attempted to identify any sources of climate model error, and reported that precipitation data corrected by a statistical correction method can be a good predictor for the observed data set at a global scale. Teng et al. [7] assessed the performances of several bias correction methods for precipitation data, and evaluated their impact on a runoff model. They reported that the quantile mapping (QM) and two-state gamma distribution mapping methods provide good performance. The QM method shows better performance than a simpler bias correction for the mean and variation in the precipitation data [8][9][10]. Themeßl et al. [11] reported that QM leads to the best performance for precipitation, particularly to large amounts of quantiles. While the QM method provides a good performance for the bias correction of stationary data, it leads to less reliable results for nonstationary data, such as simulation data under a climate change scenario. To address this drawback, Cannon et al. [12] suggested the quantile delta mapping (QDM) method which explicitly preserves relative changes in all of the quantiles of the distribution. They claimed that the QDM method is superior to the traditional QM method and the detrended quantile mapping (DQM) method, which considers trends in the mean.The QM method assumes that the distribution of simulated or estimated data preserves the distribution of any observed data. In QM, simulated data corresponding to a given probability is replaced by an observed quantile corresponding to the same probabili...

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An hourly‐scale scenario‐neutral flood risk assessment in a mesoscale catchment under climate change

Kim

Chun

Aikins

2018

Hydrological Processes

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Reliable assessments of flood risks are essential to formulate robust adaptation policies to climate change. Although the scenario-neutral flood assessments have reduced the dependency on uncertain climate predictions, coarse temporal resolutions of rainfall-runoff modelling adopted for the stress tests may introduce appreciable bias to flood risks driven by climatic stresses. To refine the temporal scale of flow estimates, this study proposes to incorporate a multiplicative random cascade (MRC) scheme and a simple catchment model in the bottom-up flood risk assessment. Results showed that use of a daily flow indicator for the stress tests could considerably underestimate the impact of climatic change on flood risks. The non-linearity between daily and hourly peak flows could increasingly amplify flood risks as the mean and variance of daily precipitation become greater in the study catchment. The first-order catchment model combined with the MRC could acceptably estimate hourly peak flows and the catchment recession behaviours at high flows. This study suggests that subdaily flood indicators should be used in the scenario-neutral assessments for small or mesoscale catchments to prevent underprediction of flood risks. To expand the applicability of the bottom-up framework, we also suggest developing efficient tools that can perturb high-resolution weather time series for the stress tests. KEYWORDS climate change and flood risks, peak flow responses to climate change, scenario-neutral flood assessment, temporal scales of the response surfaces

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Intercomparison of projected changes in climate extremes for South Korea: application of trend preserving statistical downscaling methods to the CMIP5 ensemble

Cited by 86 publications

References 60 publications

The Köppen‐Trewartha Climate‐Type Changes Over the CORDEX‐East Asia Phase 2 Domain Under 2 and 3 °C Global Warming

The Köppen‐Trewartha Climate‐Type Changes Over the CORDEX‐East Asia Phase 2 Domain Under 2 and 3 °C Global Warming

Probability Distributions for a Quantile Mapping Technique for a Bias Correction of Precipitation Data: A Case Study to Precipitation Data Under Climate Change

An hourly‐scale scenario‐neutral flood risk assessment in a mesoscale catchment under climate change

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