Consistency of extreme temperature changes in China under a historical half-degree warming increment across different reanalysis and observational datasets

Zhao, Siyao; Zhou, Tianjun; Chen, Xiaolong

doi:10.1007/s00382-020-05128-2

Cited by 22 publications

(14 citation statements)

References 76 publications

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“…The weighted changes at each grid point fall in the corresponding bin of the PDF derived from the nonparametric assessment of the PDF. The spatial PDF was proposed by Fischer et al (2013) and used for the detection of extreme climate events (e.g., Fischer & Knutti, 2014; Zhao et al, 2020; Zhao & Zhou, 2019).…”

Section: Data and Analysis Methodsmentioning

confidence: 99%

Projected Changes in the Annual Range of Precipitation Under Stabilized 1.5°C and 2.0°C Warming Futures

et al. 2020

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Changes in hydrological cycle under 1.5°C and 2.0°C warming are of great concern on the post-Paris Agreement agenda. In particular, the annual range of precipitation, that is, the difference between the wet and dry seasons, is important to society and ecosystem. This study examines the changes in precipitation annual range using the Community Earth System Model low-warming (CESM-LW) experiment, designed to assess climate change at stabilized 1.5°C and 2.0°C warming levels. To reflect the exact annual range in different regions, wet and dry seasons are defined for each grid point and year. Based on this metric, the precipitation annual range would increase by 3.90% (5.27%) under 1.5°C (2.0°C) warming. The additional 0.5°C of warming would increase annual range of precipitation by 1.37%. The enhancement is seen globally, except in some regions around the subtropics. Under the additional 0.5°C of warming, a significant increase in the annual range occurs over 15% (22%) of the ocean (land) regions. The increase is associated with the enhanced precipitation during wet season. Moisture budget analysis shows that the enhancement in annual range is dominated by vertical moisture advection, which includes thermodynamic (TH, moisture) and climate dynamic (CD, circulation changes) terms. The TH term plays a dominant role, while the CD term partly offsets the effects of the TH term. The TH term dominates over most regions except for part of the tropical ocean and some of the land regions, where the CD term is also remarkable. Thus, the enhancement of the annual range of precipitation is mainly caused by the increase in moisture. Plain Language Summary The Paris Agreement proposed a target to limit global warming to less than 2°C and pursue efforts to limit warming to less than 1.5°C. Since then, great effort has been devoted to exploring the impacts of the 1.5°C and 2.0°C warming scenarios. The changes in the seasonal cycles of precipitation have important impacts on natural and human systems but are unknown under 1.5°C and 2°C warming levels. We focus on the changes in the annual range of precipitation, which represents seasonal cycle and is defined as the difference between wet and dry seasonal rainfall in which the wet and dry seasons vary spatially and temporally. We project the changes by using CESM low-warming experiments. In the experiments, the multiyear global mean surface temperatures will stabilize at 1.5°C and 2.0°C above the preindustrial level by the end of the 21st century. We find significant increases in seasonal cycle over both ocean and land. The increase is dominated by the enhancement of precipitation during wet season. The increased seasonal cycle is caused by the increase in water vapor over most regions except for part of the tropical ocean and some of the land regions, where the effects of circulation changes are remarkable.

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Section: Data and Analysis Methodsmentioning

confidence: 99%

Projected Changes in the Annual Range of Precipitation Under Stabilized 1.5°C and 2.0°C Warming Futures

et al. 2020

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“…Although GCDs have shown to be excellent tools in climate science, these can present important limitations that should be accounted for in epidemiological assessments. For example, global GCDs are prone to measurement error, particularly in areas proximal to the sea and/or with large differences in elevation, such as mountainous regions, due to the resolution and mixed pixel coverage (land‐sea mask), or in areas with a sparse monitor network (Donat et al., 2014; Rodríguez‐Vega et al., 2018; Soares et al., 2012; Zhao et al., 2020). This can be particularly important when using GCDs with coarser spatial resolution (e.g., 30 km grid), as modeled temperature could be highly influenced by factors (e.g., orography) that eventually only affect specific areas, possibly less populated.…”

Section: Introductionmentioning

confidence: 99%

“…However, to date, no study has critically assessed the benefits of using population‐weighted temperature series from GCDs of variable spatial resolution in an epidemiological context. It is therefore imperative to explore whether application of different exposure datasets with different characteristics, such as spatial resolution could yield similar results across areas with different characteristics (Rodríguez‐Vega et al., 2018; Zhao et al., 2020).…”

Section: Introductionmentioning

confidence: 99%

A Comparative Analysis of the Temperature‐Mortality Risks Using Different Weather Datasets Across Heterogeneous Regions

et al. 2021

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New gridded climate datasets (GCDs) on spatially resolved modeled weather data have recently been released to explore the impacts of climate change. GCDs have been suggested as potential alternatives to weather station data in epidemiological assessments on health impacts of temperature and climate change. These can be particularly useful for assessment in regions that have remained understudied due to limited or low quality weather station data. However to date, no study has critically evaluated the application of GCDs of variable spatial resolution in temperature-mortality assessments across regions of different orography, climate, and size. Here we explored the performance of populationweighted daily mean temperature data from the global ERA5 reanalysis dataset in the 10 regions in the United Kingdom and the 26 cantons in Switzerland, combined with two local high-resolution GCDs (HadUK-grid UKPOC-9 and MeteoSwiss-grid-product, respectively) and compared these to weather station data and unweighted homologous series. We applied quasi-Poisson time series regression with distributed lag nonlinear models to obtain the GCD- and region-specific temperature-mortality associations and calculated the corresponding cold- and heat-related excess mortality. Although the five exposure datasets yielded different average area-level temperature estimates, these deviations did not result in substantial variations in the temperature-mortality association or impacts. Moreover, local population-weighted GCDs showed better overall performance, suggesting that they could be excellent alternatives to help advance knowledge on climate change impacts in remote regions with large climate and population distribution variability, which has remained largely unexplored in present literature due to the lack of reliable exposure data.Plain Language Summary Thus far, most studies attempting to study the impact of heat and cold on health have used data from weather stations around cities as a proxy for the temperature exposure of a population. Recently, new spatially resolved weather datasets have been released, which provide continuous temperature measurements at local or global scale, and can be particularly useful for supplying data in regions with limited or low quality weather station data. In this study, we aimed to explore the performance of these newly developed exposure datasets compared to weather stations in the United Kingdom and Switzerland, two regions which are heterogeneous in terms of topography and population distribution. We found that despite different temperature observations the datasets yield very similar results. In particular, high-resolution population-weighted temperature datasets showed better performance and thus it can be a good alternative to weather stations, especially in densely populated urban areas with large intracity temperature variability. DE SCHRIJVER ET AL.

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“…However, the risks could be substantially avoided for China by limiting global warming to 1.5°C (e.g., Chen & Sun, 2018; Li, Zhou, et al., 2018; Lin et al., 2018; Yang et al., 2018). Although the impacts of 1.5°C and 2°C warming on hot extremes over China have been assessed, most conclusions are based on a “time sampling” method through a comparison of two periods with their multiyear mean global temperature difference being 0.5°C (Chen et al., 2018; Zhao et al., 2020). However, the “time sampling” method fails to capture the time‐lagged responses to global warming (He et al., 2019; James et al., 2017; Schleussner et al., 2016).…”

Section: Introductionmentioning

confidence: 99%

“…Although the impacts of 1.5°C and 2°C warming on hot extremes over China have been assessed, most conclusions are based on a "time sampling" method through a comparison of two periods with their multiyear mean global temperature difference being 0.5°C Zhao et al, 2020). However, the "time sampling" method fails to capture the time-lagged responses to global warming (He et al, 2019;James et al, 2017;Schleussner et al, 2016).…”

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confidence: 99%

Does Dynamic Downscaling Modify the Projected Impacts of Stabilized 1.5°C and 2°C Warming on Hot Extremes Over China?

Qiu

et al. 2021

Geophysical Research Letters

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Impacts of stabilized 1.5 o C and 2 o C global warming on hot extremes over China are examined based on dynamic downscaling Dynamic downscaling can significantly modify the projection of hot extreme changes in response to the 1.5 o C and 2 o C warming The downscaling induced differences in hot extremes changes are mainly attributed to the modification of shortwave radiation changes Accepted Article This article has been accepted for publication and undergone full peer review but has not been through the copyediting, typesetting, pagination and proofreading process, which may lead to differences between this version and the Version of Record. Please cite this article as

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Consistency of extreme temperature changes in China under a historical half-degree warming increment across different reanalysis and observational datasets

Cited by 22 publications

References 76 publications

Projected Changes in the Annual Range of Precipitation Under Stabilized 1.5°C and 2.0°C Warming Futures

Projected Changes in the Annual Range of Precipitation Under Stabilized 1.5°C and 2.0°C Warming Futures

A Comparative Analysis of the Temperature‐Mortality Risks Using Different Weather Datasets Across Heterogeneous Regions

Does Dynamic Downscaling Modify the Projected Impacts of Stabilized 1.5°C and 2°C Warming on Hot Extremes Over China?

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