Anthropogenic Influences on the Persistent Night-Time Heat Wave in Summer 2018 over Northeast China

Ren, Liwen; Wang, Dongqian; An, Ning; Ding, Shuoyi; Yang, Kui; Freychet, Nicolas; Tett, Simon F. B.; Dong, Buwen; Lott, Fraser C.

doi:10.1175/bams-d-19-0152.1

Cited by 29 publications

(10 citation statements)

References 12 publications

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“…[ 51 ] on the 2013 extreme hot summer in eastern China, many studies have investigated the human influence on high-temperature events in different regions of China. These studies have used different analytical methods including simulations from atmospheric-only or coupled climate models, different metrics including the number of warm spring days [ 84 ], the number of summer heatwave days [ 85 ], the maxima for daily maximum and minimum temperatures [ 86 ] and the number of consecutive high-temperature events [ 87 ]. These studies consistently show that anthropogenic forcings have substantially increased the probability of high-temperature events in China.…”

Section: Attribution Of Extreme Eventsmentioning

confidence: 99%

Understanding human influence on climate change in China

Sun

Zhang

Ding

et al. 2021

National Science Review

101

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China's climate has been warming since the 1950s, with surface air temperature increasing at a rate higher than the global average. Changes of the climate have exerted substantial impacts on water resources, agriculture, ecosystems, and human health. Attributing past changes to causes provides a scientific foundation for national and international climate policies. Here, we review recent progress in attributing the observed climate changes over past decades in China. Anthropogenic forcings, dominated by greenhouse gas emissions, are the main drivers for the observed increases in mean and extreme temperatures. Evidence of the effect of anthropogenic forcings on precipitation is emerging. Human influence has increased the probability of extreme heat events, and has likely changed the occurrence probabilities for some heavy precipitation events. The way a specific attribution question is posed and the conditions under which the question is addressed present persistent challenges for appropriately communicating attribution results to non-specialists.

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Section: Attribution Of Extreme Eventsmentioning

confidence: 99%

Understanding human influence on climate change in China

Sun

Zhang

Ding

et al. 2021

National Science Review

101

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“…Temperature extremes associated with global warming are of great concern because our society is susceptible to both the intensity and frequency of extreme temperature. The extreme temperature has been widely reported in China (Hou et al 2014;Chen and Sun 2014;Ren et al 2020). In general, changes in extreme temperature and their influence on our society differ with the region and the category of extremes (IPCC 2012).…”

Section: Introductionmentioning

confidence: 99%

Assessment of CMIP6 and CMIP5 model performance for extreme temperature in China

Luo

Guo

Gao

et al. 2020

Atmospheric and Oceanic Science Letters

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Using the historical simulations from 27 models in phase 5 of the Coupled Model Intercomparison Project (CMIP5) and 27 models in phase 6 (CMIP6), the authors evaluated the differences between CMIP5 and CMIP6 models in simulating the climate mean of extreme temperature over China through comparison with observations during 1979-2005. The CMIP6 models reproduce well the spatial distribution of annual maxima of daily maximum temperature (TXx), annual minima of daily minimum temperature (TNn), and frost days (FD). The model spread in CMIP6 is reduced relative to CMIP5 for some temperature indices, such as TXx, warm spell duration index (WSDI), and warm days (TX90p). The multimodel median ensembles also capture the observed trend of extreme temperature. However, the CMIP6 models still have low skill in capturing TX90p and cold nights (TN10p) and have obvious cold biases or warm biases over the Tibetan Plateau. The ability of individual models varies for different indices, although some models outperform the others in terms of the average of all indices considered for different models. By comparing different version models from the same organization, the updated CMIP6 models show no significant difference from their counterparts from CMIP5 for some models. Compared with individual models, the median ensembles show better agreement with the observations for temperature indices and their means.

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“…Additionally, our conclusions are only based on daily observed rainfall from CMA and ensembles from a single atmospheric model forced by observed SST or SIC with and without anthropogenic warming. Multiple observational datasets (Hegerl et al 2015) and a comparison with estimates from fully coupled models (Sun et al 2014;Massey et al 2015;Ren et al 2020) are needed to test our results, as ocean-atmosphere interaction is important for East Asian climate (Wang et al 2005).…”

Section: Conclusion and Discussionmentioning

confidence: 98%