Assessment and Prediction of Extreme Temperature Indices in the North China Plain by CMIP6 Climate Model

Wang, Hui; Wang, Lu; Yan, Guoying; Bai, Haiyan; Zhao, Yihong; Ju, Minmin; Xu, Ximing; Jin, Yan; Xiao, Dengpan; Chen, Lirong

doi:10.3390/app12147201

Cited by 8 publications

(6 citation statements)

References 19 publications

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“…With global warming, studies suggest an increased frequency of extreme weather events in the Jiaodong Peninsula [58]. Extreme droughts, floods, frosts, and other natural disasters can lead to reduced vegetation coverage, decreased crop yields, and other ecological issues [59].…”

Section: Impact Of Natural Factors On Eeqmentioning

confidence: 99%

Spatiotemporal Evolution of Ecological Environment Quality and Driving Factors in Jiaodong Peninsula, China

Guo,

Wang

2024

Sustainability

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To monitor the Ecological Environment Quality (EEQ) of the Jiaodong Peninsula and provide a scientific basis for ecological environment governance and sustainable development in the region, this study evaluates the EEQ of Jiaodong using the Remote Sensing-based Ecological Index (RSEI) model and analyzes its spatiotemporal evolution patterns, building upon single-factor correlation analysis using the Random Sample Consensus (RANSAC) algorithm, using GeoDetector to analyze the driving mechanisms of human activities and natural factors in EEQ. The results indicate the following: (1) The average RSEI values for 2000, 2010, and 2020 are 0.60, 0.57, and 0.66, with Good or Excellent areas accounting for 56.48%, 51.02%, and 67.17%. From 2000 to 2020, RSEI strong improvement areas were predominantly distributed in the eastern hilly areas of the Jiaodong Peninsula. The RSEI of the Jiaodong Peninsula showed significant spatial autocorrelation. (2) The RANSAC algorithm effectively reduces noise interference in remote sensing data, thereby improving the accuracy of single-factor correlation analysis. (3) In 2000, the importance of natural factors exceeds that of human activity factors. The Standardized Precipitation Evapotranspiration Index (SPEI) is the most important driving factor; while in 2010 and 2020, human activity factors surpass natural factors in importance, with the Land Use Composite Index (LUCI) being the most significant driving factor. The driving factors exhibited double-factor and nonlinear enhancement. The most significant interaction factors affecting the EEQ of the Jiaodong Peninsula in 2000, 2010, and 2020 are SPEI∩GDP, LUCI∩SPEI, and LUCI∩GDP.

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Section: Impact Of Natural Factors On Eeqmentioning

confidence: 99%

Spatiotemporal Evolution of Ecological Environment Quality and Driving Factors in Jiaodong Peninsula, China

Guo,

Wang

2024

Sustainability

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“…In addition, in North China, historical long-term average climate change shows a warming and drying trend in the last 30 years [35,65], and the warming rate is much higher than in the Northern Hemisphere [72]. Moreover, in the next three decades, the temperature in this region is more likely to increase at an even higher rate [29,32].…”

Section: Variations In Extreme-high-temperature Eventsmentioning

confidence: 99%

“…This study provides valuable insights into potential prevention measures aimed at safeguarding the North China ecosystem, which can aid government managers in taking effective action to protect local ecosystems and minimize the eco-economic losses caused by climate extremes. Moreover, given the anticipated long-term warming trend and its associated proliferation of heatwaves that adversely influence vegetation [29,32,87], local authorities are advised to proactively implement adaptive mitigation strategies to prevent the potential negative effects of extreme high temperatures during summer months, particularly in June. Furthermore, our findings further underscore the vulnerability of semi-arid and arid grassland ecosystems to extreme high temperatures.…”

Section: Impacts Of Extreme-high-temperature Events On the Ndvimentioning

confidence: 99%

“…In the past, North China has had a warming and drying trend in terms of climate and has experienced several large-scale drought disasters [29,30]. Notably, extreme-high-temperature events in North China have exhibited an obvious increasing trend [31,32]. By the end of the 21st century, it is expected that the North China Plain will be subjected to deadly heatwaves, with the wet-bulb temperature exceeding the threshold for outdoor agricultural labor [29,33].…”

Section: Introductionmentioning

confidence: 99%

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Impacts of Extreme-High-Temperature Events on Vegetation in North China

Yang,

Jiang,

Ding

2023

Remote Sensing

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Understanding the response of vegetation to temperature extremes is crucial for investigating vegetation growth and guiding ecosystem conservation. North China is a vital hub for China’s economy and food supplies, and its vegetation is highly vulnerable to complex heatwaves. In this study, based on remote sensing data, i.e., the normalized difference vegetation index (NDVI), spatio-temporal variations in vegetation and extreme high temperatures are investigated by using the methods of trend analysis, linear detrending, Pearson correlation and ridge regression. The impacts of extreme-high-temperature events on different vegetation types in North China from 1982 to 2015 are explored on multiple time scales. The results indicate that the NDVI in North China exhibits an overall increasing trend on both annual and monthly scales, with the highest values for forest vegetation and the fastest growth trend for cropland. Meanwhile, extreme-high-temperature events in North China also display an increasing trend. Before detrending, the correlations between the NDVI and certain extreme-high-temperature indices are not significant, while significant negative correlations are observed after detrending. On an annual scale, the NDVI is negatively correlated with extreme temperature indices, except for the number of warm nights, whereas, on a monthly scale, these negative correlations are only found from June to September. Grassland vegetation shows relatively strong correlations with all extreme temperature indices, while forests show nonsignificant correlations with the indices. This study offers new insight into vegetation dynamic variations and their responses to climate in North China.

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“…More detailed information about these GCMs can be found at https://esgf-node.llnl.gov/projects/input4mips/. The future scenario in CMIP6 is a combination of different representative concentration pathways (RCPs) and shared socioeconomic pathways (SSPs) (H. Wang, Wang, et al, 2022). Three integrated scenarios, SSP1‐2.6 (SSP1 + RCP2.6), SSP2‐4.5 (SSP2 + RCP4.5), and SSP5‐8.5 (SSP5 + RCP8.5), were used in this study.…”

Section: Study Area and Datamentioning

confidence: 99%

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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Assessment and Prediction of Extreme Temperature Indices in the North China Plain by CMIP6 Climate Model

Cited by 8 publications

References 19 publications

Spatiotemporal Evolution of Ecological Environment Quality and Driving Factors in Jiaodong Peninsula, China

Spatiotemporal Evolution of Ecological Environment Quality and Driving Factors in Jiaodong Peninsula, China

Impacts of Extreme-High-Temperature Events on Vegetation in North 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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