Changes of extreme climate events and related risk exposures in Huang‐Huai‐Hai river basin under 1.5–2°C global warming targets based on high resolution combined dynamical and statistical downscaling dataset

Abstract

Extreme climate events and related risk exposures in Huang‐Huai‐Hai (HHH) river basin were projected under global warming of 1.5–2°C using the high‐resolution combined dynamical and statistical downscaling dataset. Firstly, evaluation indicated that the dataset can well reproduce the spatial distribution of all temperature extremes and most of the precipitation extremes, providing a reliable ability for future projections. Then, projections showed that the hot events were projected to increase, while the cold events were projected to decrease substantially in the whole HHH river basin for 1.5°C and 2°C global warming. The additional 0.5°C of warming roughly accelerated the increase of extreme temperatures by 0.6°C, and increased the number of heat days by 2.7 days. In addition, the rainfall events and the precipitation intensity were projected to increase while the drought events were projected to decrease, with the slight changes due to the additional 0.5°C warming. The gross domestic product (GDP) exposures to heat events and heavy rainfall increased more than nine fold for 1.5°C and 2°C global warming, while the growing population (POP) exposures to that increased by more than 100% and 20%, respectively. For the increased exposures to heat events, the changes in GDP (POP) were as important as changes in interaction effect between climate and GDP (POP), while the exposures to heavy rainfall events was mainly dominated by GDP (POP). In addition, the enhanced interaction effect was the most important factor to the increase of exposures due to additional 0.5°C of warming. Notably, the largest increases of heat events and heavy rainfall events were projected in Huaihe river basin due to the additional 0.5°C warming, accompanied by greatest increase of exposures, implying larger risk in the future.

Future Projection of Solar Energy Over China Based on Multi‐Regional Climate Model Simulations

“…Based on the framework of the East Asia domain from CORDEX phase II (Giorgi et al, 2009), the simulation domain here encompassed the whole of continental China and adjacent areas (Figure 1). Previous studies have reported detailed information about the model parameterization schemes, evaluation results, and applied studies (Gao et al, 2017(Gao et al, , 2018Han et al, 2019;Shi et al, 2018;Wu et al, 2020Wu et al, , 2021. To examine the regional changes of solar energy, we divided China into eight subregions, as per China's National Assessment Report on Climate Change (National Report Committee, 2011;Zhou et al, 2015) (Figure 1): NEC (northeast China), NC (north China), EC (east China), CC (central China), SC (south China), SWC1 (Tibetan Plateau), SWC2 (southwest China), and NWC (northwest China).…”

“…Solar radiation (SR), surface temperature (TM), surface wind speed (SW) results were obtained from the above sets of dynamical downscaling outputs. Their ensemble mean (hereafter referred to as ENS) was calculated using the equal‐weighted average method (Han et al., 2019; Wu et al., 2021). To comprehensively validate the model performance, two types of reference data were used.…”

“…The results showed that overestimation and uncertainty in GCMs with respect to the projection of solar radiation is remarkable, due to the limitations related to certain physical processes in these models (Xiao et al., 2019). Therefore, dynamical downscaling based on regional climate models (RCMs) is in demand, which was been proven to have the potential to add value and provide more reliable signals of change owing to the finer resolutions involved (Gao et al., 2017; Giorgi et al., 2009; Ji & Kang, 2015; Niu et al., 2015; Sawadogo et al., 2021; Shi et al., 2018; Wu et al., 2021, 2020; Yu et al., 2015).…”

Evaluation of Bias Correction Methods for Regional Climate Models: Downscaled Rainfall Analysis Over Diverse Agroclimatic Zones of India

“…However, the RCMs also exhibit systematic biases that are likely dependent on the GCM boundary and model internal dynamics (Giorgi, 2007; Teutschbein & Seibert, 2012). The errors in RCMs, despite their high spatial resolution, lead to large uncertainty in projection studies and limit their application in regional climate change impacts studies (Cubasch et al., 2001; Kattenberg, 1996; Pastén‐Zapata et al., 2020; Stocker et al., 2013; Van der Linden & Mitchell, 2009; Wu et al., 2021).…”

Evaluation and analysis of the projected population of China

“…To accurately grasp China's future population growth tendency, the projections of NUIST (Nanjing University of Information Science and Technology) and THU (Tsinghua University) were created recently by Huang et al 21 and Chen et al 22 , respectively. These spatially explicit population datasets have been widely used to explore the influence of future population levels on global climate change 23 – 26 , extreme weather disaster events 27 – 29 , land-use change 24 , and ecosystem service change 30 . Although they have been applied in many research fields, we know little about their projection accuracy and poorly understand the factors that affect their projection accuracy.…”