How Extreme Events in China Would Be Affected by Global Warming—Insights From a Bias‐Corrected CMIP6 Ensemble

Abstract

The Paris Agreement adopted by 196 Parties at COP21 in 2015 set a global warming goal, which is to keep global warming to well below 2°C, preferably to 1.5°C, relative to the pre-industrial period (IPCC, 2018). To achieve this goal, every participating country has proposed its emission reduction roadmap and made the corresponding policies. As the largest developing country and one of the most carbon emitters, China proposed its target in

Performance-Based Evaluation of CMIP5 and CMIP6 Global Climate Models and Their Multi-Model Ensembles to Simulate and Project Seasonal and Annual Climate Variables in the Chungcheong Region of South Korea

“…The statistic S is assumed to be normally distributed when n ≥ 8, with the mean and variance represented by Equations ( 16) and (17), respectively.…”

“…However, to the best of our knowledge, there are currently limited studies on the Chungcheong region of South Korea despite the vulnerability of this region to extreme events of climate change. While the outcomes of GCM studies are essential for climate change impact assessment, complex topographic features and seasonal variability are among the significant factors that affect the accuracy of the results in many regions, including the Chungcheong region, and consequently increase the uncertainties in climate change assessment and projections [17,18]. Meanwhile, an accurate projection of climate variables is crucial for predicting future climate change, especially at the sub-region level, due to their significantly varied impacts associated with a local climate.…”

Assessing the Potential Impacts of Climate Change on Current Coastal Ecosystems—A Canadian Case Study

“…ML can be used to classify remotely sensed images efficiently and precisely because its capabilities include the ability to handle high-dimensional data and map classes with very complicated characteristics [27]. The application of ML in environmental studies has been explored widely in the past, including in studies of land use and land cover change [28,29], climate change [30][31][32], and drone-based applications for change detection [33,34]. Various ML algorithms that are popularly used to deal with complex problems in ecosystem research include the random forest (RF) algorithm, the support vector machine (SVM) algorithm, artificial neural networks (ANNs), decision trees (DTs), the K-nearest neighbors (KNN) algorithm, principal component analysis (PCA), and clustering algorithms (CAs), as well as other approaches as reviewed by Bansal et al [35] and Ray [36].…”

Projected changes in daily precipitation, temperature and wet‐bulb temperature across Arizona using statistically downscaled CMIP6 climate models

“…Similarly, Babaousmail et al (2022) used quantile mapping to investigate the projected changes in extreme precipitation over the Mediterranean and Sahara regions, and emphasized that wet areas are becoming wetter and dry areas drier. Guo et al (2023) performed downscaling and bias correction for temperature and precipitation from 12 CMIP6 models across China. They employed an improved quantile mapping known as the equidistant cumulative distribution function matching approach; they analysed the occurrence of extreme events using the 99% percentile of the joint precipitation and temperature distributions, concluding the risk of extremes in China is greater for both univariate and concurrent extreme events.…”