Impact of bias correction of regional climate model boundary conditions on the simulation of precipitation extremes

Kim, Youngil; Rocheta, Eytan; Evans, Jason P.; Sharma, Ashish

doi:10.1007/s00382-020-05462-5

Cited by 35 publications

(27 citation statements)

References 39 publications

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“…As shown in previous studies (Kim et al, 2020;Rocheta, Evans, & Sharma, 2017), while bias correction can produce consistent improvement in mean and extreme rainfall characteristics, other biases within the simulations remain.…”

supporting

confidence: 74%

“…The results of multivariate dependence highlight that both RCM simulations with univariate bias-corrected and uncorrected GCM boundary conditions do not show substantial improvement for all atmospheric variables, indicating that the mismatch in physical relationships between the atmospheric variables is not dampened sufficiently through the relaxation zone. What is surprising is that while the bias correction shows good performance for temporal and spatial aspects, there are still large differences in multivariate dependence which may lead to substantial anomalies in the simulation, particularly for extreme events (Kim et al, 2020;Lepore et al, 2016). Several multivariate bias correction options are now available in the literature (François et al, 2020;Mehrotra & Sharma, 2015;Sharma & Mehrotra, 2016) which should be further investigated to address all systematic biases when undertaking bias correction of atmospheric variables.…”

Section: Discussionmentioning

confidence: 99%

“…In this study, the climatological means were calculated at the monthly scale over 31 years, meaning the climatological component of the GCM was transformed to that of the reanalysis data using 12 monthly correction factors. More details can be found in Kim et al (2020) and Rocheta, Evans, and Sharma (2017).…”

Section: Mean Bias Correctionmentioning

confidence: 99%

“…The correction factors were calculated on monthly and yearly time scales, meaning the climatological means were calculated from the 31 years GCM data for each month. More details regarding the correction can be found in Kim et al (2020) and Rocheta, Evans, and Sharma (2017).…”

Section: Nested Bias Correctionmentioning

confidence: 99%

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Spatial, Temporal, and Multivariate Bias in Regional Climate Model Simulations

Kim

Evans

Sharma

et al. 2021

Geophysical Research Letters

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High-resolution climate model simulations are necessary to assess changes in precipitation at the fine spatial scale important for decision making. While global climate models (GCMs) are used to derive coarsescale information, the direct use of GCM simulations is limited as their temporal and spatial resolution are generally insufficient to use at the regional scale (Caldwell et al., 2009). Limited area regional climate models (RCMs), driven by boundary conditions from GCM simulations, are widely used to overcome this problem. Unfortunately, their use is hindered by inherent systematic bias in GCMs which can be transferred through the boundary conditions imparting bias into the RCM (

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supporting

confidence: 74%

Section: Discussionmentioning

confidence: 99%

Section: Mean Bias Correctionmentioning

confidence: 99%

Section: Nested Bias Correctionmentioning

confidence: 99%

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Spatial, Temporal, and Multivariate Bias in Regional Climate Model Simulations

Kim

Evans

Sharma

et al. 2021

Geophysical Research Letters

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“…It is noted that the lateral conditions based on the GCM output have a significant impact on the accuracy of the dynamic downscaling (Diaconescu et al, 2007;Ludwig et al, 2017;Kim et al, 2020). In this study, the CMIP5 GCM simulations are used to get the initial and time-evolving lateral boundary conditions for RegCM4, it is expected that the latest model output of state-ofthe-art CMIP6 GCMs (Eyring et al, 2016) will be applied to drive the RegCM4 for future climate change projection in river basins.…”

Section: Discussionmentioning

confidence: 99%

Projected Changes in Precipitation Extremes Over Jiulongjiang River Basin in Coastal Southeast China

Dike

Lin

et al. 2021

Front. Earth Sci.

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The southeast coastal region of China is susceptible to challenges related to extreme precipitation events; hence, projection of future climate extremes changes is crucial for sustainable development in the region. Using the Regional Climate Model Version 4 (RegCM4), the future changes of summer precipitation extremes have been investigated over the Jiulongjiang River Basin (JRB), a coastal watershed in Southeast China. Comparison between the RegCM4 simulated and observed rainy season precipitation over JRB suggests that the RegCM4 can reasonably reproduce the seasonal precipitation cycle, the frequency distribution of precipitation intensity, and the 50-year return levels of precipitation extremes over JRB. Furthermore, the model projects an increase in daily maximum rainfall (RX1day) mostly over the northern part of the basin and a decrease over other parts of the basin, while projecting a widespread decrease for maximum consecutive 5-day precipitation amount (RX5day) relative to the present day. In terms of the 50-year return level of RX1day (RL50yr_RX1day), a general increase is projected over most parts of the basin in the near and far future of the 21st century, but a decrease can be found in the northeast and southwest parts of the JRB in the mid-21st century. The future change of the 50-year return level of RX5day (RL50yr_RX5day) shows a similar spatial pattern with that of RL50yr_RX1day in the near and mid-21st century, but with a larger magnitude. However, a remarkable decrease in RL50yr_RX5day is found in the south basin in the far future. Meanwhile, the projected changes in the 50-year return level for both RX1day and RX5day differ between the first and second rainy seasons in JRB. Specifically, the future increase in RL50yr_RX5day over the north basin is mainly contributed by the changes during the first-half rainy season, while the decrease of RL50yr_RX5day in the south is mostly ascribed to the future changes during the second-half rainy season. All above results indicate that the future changes of precipitation extremes in JRB are complicated, which might differ from extreme indices, seasons, and future projected periods. These will thus be of practical significance for flood risk management, mitigation, and adaptation measures in Jiulongjiang River Basin.

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Projection of future precipitation change using CMIP6 multimodel ensemble based on fusion of multiple machine learning algorithms: A case in Hanjiang River Basin, China

Wang,

Liu,

Luan

et al. 2023

Meteorological Applications

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Projecting precipitation changes is essential for researchers to understand climate change impacts on hydrological cycle. This study projected future precipitation over the Hanjiang River Basin (HRB) based on the multimodel ensemble (ME) of six global climate models from Phase 6 of the Coupled Model Intercomparison Project (CMIP6). An ME method using the fusion of four machine learning (ML) algorithms (random forest [RF], K‐nearest neighbors [KNN], extra tree [ET], and gradient boosting decision tree [GBDT]) was proposed in this study. The future precipitation changes were investigated during 2023–2042 (Near‐term), 2043–2062 (Mid‐term), and 2081–2100 (Long‐term) periods, with reference to the base period 1995–2014, under three integrated scenarios (SSP1‐2.6, SSP2‐4.5, and SSP5‐8.5) of the Shared Socioeconomic Pathways (SSPs) and the representative concentration pathways (RCPs). The results show that: (1) the proposed ME method performs better than the ME mean and individual ML algorithms, with a correlation coefficient value reaching 0.88 and Taylor skill score reaching 0.764. (2) The precipitation under SSP5‐8.5 has the largest upward trend with the annual precipitation variation range of −9.27% to 112.84% from 2023 to 2100, followed by SSP2‐4.5 with −30.48% to 44.67%, and the smallest under SSP1‐2.6 with −37.19% to 37.78%, which show a significant trend of humidification over the HRB in the future. (3) The precipitation changes over the HRB are projected to increase over time, with the largest in the Long‐term, followed by Mid‐term, and the smallest in the Near‐term. (4) The northeastern parts of the HRB are projected to experience a large precipitation in the future, and the southeastern parts are smaller. (5) Uncertainties in the projected precipitation over the HRB still exist, which can be reduced by ME. The findings obtained in this study have important implications for hydrological policymakers to make adaptive strategies to reduce the risks of climate change.

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Impact of bias correction of regional climate model boundary conditions on the simulation of precipitation extremes

Cited by 35 publications

References 39 publications

Spatial, Temporal, and Multivariate Bias in Regional Climate Model Simulations

Spatial, Temporal, and Multivariate Bias in Regional Climate Model Simulations

Projected Changes in Precipitation Extremes Over Jiulongjiang River Basin in Coastal Southeast China

Projection of future precipitation change using CMIP6 multimodel ensemble based on fusion of multiple machine learning algorithms: A case in Hanjiang River Basin, China

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