Simulating evaluation and projection of the climate zones over China by CMIP5 models

He, Wenping; Zhao, Shanshan; Wu, Qiong; Jiang, Yundi; Wan, Shiquan

doi:10.1007/s00382-018-4410-1

Cited by 28 publications

(11 citation statements)

References 56 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…South China receives the most precipitation of all regions in China. Thus, South China has the most extreme precipitation events during the preflood season (Zhai and Eskridge, 1997;Miao et al, 2015;He et al, 2018). The preflood season, which is the most important rainy season over South China, indicates the period prior to the East Asian summer monsoon onset.…”

Section: Introductionmentioning

confidence: 99%

The roles of moisture transports in intraseasonal precipitation during the preflood season over South China

Chu

Wang

Feng

2019

Intl Journal of Climatology

View full text Add to dashboard Cite

The roles of moisture sources and transport in intraseasonal precipitation variabilities during the preflood season over South China are complex and different. In this paper, ERA‐Interim reanalysis data that were collected four times daily from 1979 to 2017 are used to analyse the variation and influence of moisture transport in intraseasonal precipitation during the preflood season over South China. Using the Hybrid Single‐Particle Lagrangian Integrated Trajectory model, fixed atmospheric moisture sources are defined: land area source (LD source), Indian Ocean source (IO source), Pacific Ocean source (PO source), South China Sea source (SCS source), and South China source (SC source). Sea source moisture transport accounts for more than 75% of the total precipitation during the preflood season, which is the most important moisture source in South China. For April, southeasterly moisture transport, which includes PO source and SCS source moisture transports, is the major moisture transport over South China. Then, the IO source and SCS source precipitation increased significantly during May. During June, the IO source precipitation accounted for 43.75% of the total precipitation, thus dominating precipitation over South China. In addition, there is a significant difference in the interdecadal variation in the preflood season precipitation before and after the Indian summer monsoon onset. Before the Indian summer monsoon onset, the land‐sea thermal contrast decreased, which led to a significant interdecadal reduction in the IO source, PO source, and SCS source moisture transports. After the Indian summer monsoon onset, the increase in IO source moisture transport is the primary cause of the interdecadal increase in precipitation over South China.

show abstract

Section: Introductionmentioning

confidence: 99%

The roles of moisture transports in intraseasonal precipitation during the preflood season over South China

Chu

Wang

Feng

2019

Intl Journal of Climatology

View full text Add to dashboard Cite

show abstract

“…the slope of the curves in figure 3) are mostly similar. While multi-model ensemble simulations would increase the accuracy and the delineation of uncertainty ranges [55], the similarity of the results across models indicates that relying on a single model is sufficient for the purpose of this study. Figure 5 displays two-dimensional histograms where the color scale indicates the amount of grid cells that display a pair of given velocities for T m 2  and for the 2nd or 98th percentiles.…”

Section: Median and Extreme Quantilesmentioning

confidence: 94%

Shifting velocity of temperature extremes under climate change

Rey

Rohat

Perroud

et al. 2020

Environ. Res. Lett.

View full text Add to dashboard Cite

Rapid changes in climatic conditions threaten both socioeconomic and ecological systems, as these might not be able to adapt or to migrate at the same pace as that of global warming. In particular, an increase of weather and climate extremes can lead to increased stress on human and natural systems, and a tendency for serious adverse effects. We rely on the EURO-CORDEX simulations and focus on the the screen-level daily mean temperature (T2m). We compare the shifting velocities of the cold and hot extremes with these of the associated central trends, i.e. the arithmetical mean or median. We define the extremes relative to the T2m distribution as it evolves with time over the period of 1951-2100. We find that temperature extremes shift at a similar velocity compared to that of the central trends. Accordingly, the T2m probability distribution shifts mostly as a whole, as the tails of the distribution increase together with the central trends. Exceptions occur however in specific regions and for the clustering of warm days, which shifts slower than all other extremes investigated in this study.

show abstract

“…In contrast, the decrease of the shifting velocity with the quantile over Russia is much stronger in RACMO than in ALADIN, and smaller in REMO and HIRHAM. While multi-model ensemble simulations would increase the accuracy and the delineation of uncertainty ranges [56], the similarity of the results across models indicates that relying on a single model is sufficient for the purpose of this study. Figure 4 displays the two-dimensional histograms relating the shifting velocities ..... of the median to that of the extreme percentiles; Panels a-d focus on the velocity magnitude, while panels e-h display the shifting directions.…”

Section: Median and Extreme Quantilesmentioning

confidence: 94%

Shifting velocity of temperature extremes under climate change

Rey¹,

Rohat²,

Perroud³

et al. 2019

Preprint

View full text Add to dashboard Cite

Rapid changes in climatic conditions threaten both socioeconomic and ecological systems, as these might not be able to adapt or to migrate at the same pace as that of global warming. In particular, an increase of weather and climate extremes can lead to increased stress on human and natural systems, and a tendency for serious adverse effects. Relying on the EURO-CORDEX simulations, we compare the shifting velocities of cold and hot extremes of the screen-level daily mean temperature (T2m), with that of the associated central trends, i.e., the arithmetical mean or median. Defi?ning the extremes relative to the T2m distribution as it evolves in time over the period of 1950-2100, we ?find that temperature extremes shift at a velocity similar to that of the central trends. Indeed, the T2m probability distribution shifts as a whole, as the tails of the distribution increase together with the central trends. Exceptions however occur in specifi?c regions and for the clustering of warm days, which shifts slower than all other extremes investigated in this study.

show abstract

Simulating evaluation and projection of the climate zones over China by CMIP5 models

Cited by 28 publications

References 56 publications

The roles of moisture transports in intraseasonal precipitation during the preflood season over South China

The roles of moisture transports in intraseasonal precipitation during the preflood season over South China

Shifting velocity of temperature extremes under climate change

Shifting velocity of temperature extremes under climate change

Contact Info

Product

Resources

About