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Advanced Climate Simulation and Observation

Edited by
October 2023
416 pages
  • ISBN978-3-0365-9138-4 (Hardback)
  • ISBN978-3-0365-9139-1 (PDF)
https://doi.org/10.3390/books978-3-0365-9139-1
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This book is a reprint of the Special Issue Advanced Climate Simulation and Observation that was published in

Chemistry & Materials Science
Environmental & Earth Sciences
Summary

Global climate changes, particularly extreme events, affect terrestrial carbon, water, and energy exchanges between the atmosphere, biosphere, and lithosphere, thus controlling freshwater availability, floods, and droughts. Therefore, it is urgent and necessary to develop advanced climate simulation and observation approaches and models related to extreme climate events. Advanced climate simulation and observation can improve the accurate prediction of climate change and long-term trends, which can mitigate climate events' impacts on human society. Under these conditions, this reprint aims to introduce advanced climate simulation and observation approaches to various practical studies related to climate variations, including the global climate models (GCMs) and regional climate models (RCMs), mitigation studies of high-impact climate events, predictions of climate variations, and some new artificial intelligence. Twenty-two papers have been collected in this reprint, with eight original research articles reporting on climate change and six papers reporting on climate change's impact on society and the economy. Meanwhile, three papers reported climate change's impact on agriculture, and climate change's impact on human health was studied in five articles.

Format
  • Hardback
License
© 2022 by the authors; CC BY-NC-ND license
Keywords
hydrological modeling; gridded datasets; sensitivity analysis; water balance; snowmelt; SWAT; Upper Vakhsh River Basin; economic loss prediction; machine learning; input-output model; flooding; regional climate model; RegCM4.5; western Tianshan Mountains; parameterization scheme; air quality satisfaction; quality of life; binomial logistic regression; health utility value; experienced utility; elevated [CO2]; warming; SPAD; leaf nitrogen monitoring; nitrogen management; Issyk-Kul; SWAT; accumulated temperature; snowmelt; yield per unit area of beans; climate change; panel spatial error model; air pollution; respiratory disease; generalized additive model; scenario analysis; assessment of economic losses; arid climate; geothermal energy; underground temperature; greenhouse; heat exchanger; agricultural air pollution; labor migration; mediation effect; income effect; economy of scale; collective effect; haze pollution; scale effect; special spillover effect; urban population agglomeration; AQI; visual analysis; heat map; ARIMA model; neural network model; pulmonary tuberculosis; penalized distributed lag non-linear model; meteorological factors; apparent temperature; cumulative risk; HDI; decoupling index; carbon emission performance; LMDI; 10 m wind speed; cumulus parameterization schemes; sensitivity of physical processes; WRF; mainland China; environmental regulation; green innovation efficiency; SBM of super-efficiency; system GMM estimation; model evaluation; rainfall simulation; interannual variation; IAP-AGCM; Thailand; China; environmental Kuznets curve; geographically weighted regression; haze; spatial heterogeneity; air pollutants; sustained exposure to pollution; respiratory and cardiovascular diseases; CiteSpace; co-occurrence keywords; burst words; mountain-type zoonotic visceral leishmaniasis; climate variables; environmental variables; ecological niche model; transmission risk prediction; drought; cropland; CMIP6; exposure; scPDSI; China; weather radar nowcasting; generative adversarial network (GAN); Temporal and Spatial GAN (TSGAN); heavy precipitation; n/a