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.
Countries in East Asia have set ambitious goals for the development of wind energy to meet the increasing energy demand and to mitigate anthropogenic climate change. However, few studies have investigated changes in wind energy over East Asia under future climatic conditions. In this study, we investigate future changes of 100‐m wind speed and wind energy potential over the CORDEX‐East Asia region under the Representative Concentration Pathway (RCP) 8.5 scenario, by using ensemble simulations from the regional climate model Consortium for small‐scale modeling in CLimate Mode (CCLM). A multivariate bias adjustment method based on the N‐dimensional probability density function transform is used to correct raw simulated horizontal wind components. The comparison between future climate (2021–2050 and 2070–2099) and the present climate (1971–2000) shows decreases in wind speed, wind power density, and wind energy output over most of the CORDEX‐East Asia region, especially in the tropics. Projected increases are pronounced over the Himalayan regions, the Indo‐China Peninsula, the South China Sea, and the western Pacific Ocean in summer and over northeastern China, parts of Western China and the Indo‐China Peninsula in winter. Interannual and intra‐annual variability of wind power density are projected to intensify significantly for most of the CORDEX‐East Asia region. The occurrence of weak wind speeds (<3 m/s) is projected to increase, while strong wind speeds (>11 m/s) are projected to decrease over most of the ocean.
Hourly tide‐gauge data along the coast of China are used to evaluate changes in extreme water levels in the past several decades. Mean sea level, astronomical tide, nontidal component and the tide‐surge interaction was analyzed separately to assess their roles in the changes of extreme sea levels. Mean sea level at five tide gauges, Kanmen, Keelung, Zhapo, Xiamen and Quarrybay, show significant increasing trends during the past decades (1954–2013) with a rate of about 1.4–3.5 mm/yr. At Keelung, Kaohsiung and Quarrybay the mean high waters increased during 1954–2013 with a rate from 0.6 to 1.8 mm/yr, while the annual mean tidal range rose at the same time by 0.9 to 3.8 mm/yr. In terms of storm surge intensities, there is interannual variability and decadal variability but five tide gauges show significant decreasing trends, and three gauges, at Keelung, Xiamen and Quarrybay, exhibited significant increases of extreme sea levels with trends of 1.5–6.0 mm/yr during 1954–2013. Significant tide‐surge interactions were found at all 12 tide gauges, but no obvious change was found during the past few decades. The changes in extreme sea levels in this area are strongly related to the changes of mean sea levels (MSL). At gauges, where the tide‐surge interaction is large, the astronomic tides are also an important factor for the extreme sea levels, whereas tide gauges with little tide‐surge interaction, the changes of wind driven storm surge component adds to the change of the extreme sea levels.
In this study, we investigate the skills of the regional climate model Consortium for Small-Scale Modeling in Climate Mode (CCLM) in reproducing historical climatic features and their added value to the driving global climate models (GCMs) of the Coordinated Regional Climate Downscaling Experiment—East Asia (CORDEX-EA) domain. An ensemble of climate simulations, with a resolution of 0.44°, was conducted by downscaling four GCMs: CNRM-CM5, EC-EARTH, HadGEM2, and MPI-ESM-LR. The CCLM outputs were compared with different observations and reanalysis datasets. Results showed strong seasonal variability of CCLM’s ability in reproducing climatological means, variability, and extremes. The bias of the simulated summer temperatures is generally smaller than that of the winter temperatures; in addition, areas where CCLM adds value to the driving GCMs in simulating temperature are larger in the winter than in the summer. CCLM outperforms GCMs in terms of generating climatological precipitation means and daily precipitation distributions for most regions in the winter, but this is not always the case for the summer. It was found that CCLM biases are partly inherited from GCMs and are significantly shaped by structural biases of CCLM. Furthermore, downscaled simulations show added value in capturing features of consecutive wet days for the tropics and of consecutive dry days for areas to the north of 30°N. We found considerable uncertainty from reanalysis and observation datasets in temperatures and precipitation climatological means for some regions that rival bias values of GCMs and CCLM simulations. We recommend carefully selecting reference datasets when evaluating modeled climate means.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.