Assessment of CMIP6 in simulating precipitation over arid Central Asia

Guo, Hao; Bao, Anming; Chen, Tao; Zheng, Guoxiong; Wang, Yunqian; Jiang, Liangliang; Maeyer, Philippe De

doi:10.1016/j.atmosres.2021.105451

Cited by 104 publications

(84 citation statements)

References 54 publications

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“…However, because the data of the measured sites in some areas is less (such as the Qinghai-Tibet Plateau), it can be replaced by a data set that integrates multiple data sources (Alizadeh et al 2021;Wang et al 2019). When the data set is used as the reference data, it is also necessary to verify it with the measured data to ensure that the reference data set has high precision (Guo et al 2021a;Mondal et al 2021). For example, the CMFD data set used in this study has high precision and can be used as a standard for measuring the accuracy of GCMs in HRB.…”

Section: Discussionmentioning

confidence: 99%

“…Although the results show that the total amount of precipitation will increase in the four SSPs, while the distribution of precipitation may become more uneven. The areas of increased precipitation are mainly concentrated in the upstream and downstream areas, while the precipitation in the middle reaches where the water transfer project located has decreased, which further aggravates the shortage of water resources balance (Bağçaci et al 2021;Guo et al 2021a;Mohanty and Simonovic 2021). In addition, under future scenarios, the trend of heavy precipitation will be more obvious, which will increase the risk of flood disasters in the HRB.…”

Section: Discussionmentioning

confidence: 99%

“…A reasonable assessment of future climate change is conducive to effectively responding to the challenges brought by climate change (Alizadeh et al 2021;Jia et al 2021;Jin et al 2021;Li et al 2020a). Global climate models (GCMs) have been widely used in future climate change projections, and the model results can provide data support for research on regional and continental-scale impacts caused by climate change (Bağçaci et al 2021;Guo et al 2021a;Gupta et al 2020;Iqbal et al 2021).…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, the CMIP6 can carry out more numerical experiments according to the new numerical experiment design scheme, thereby continuing to improve the resolution of the models (Mondal et al 2021;Rivera and Arnould 2020;Shafeeque and Luo 2021). At present, the GCMs have been widely used to evaluate the impact of climate change on ecology, environment, hydrology, and agriculture (Guo et al 2021a;Yazdandoost et al 2021;Zhai et al 2020). However, due to differences in mechanism, terrain elevation settings, parameterization schemes, and spatial resolution between the GCMs, there are greater uncertainties in the output results of different models (Kim et al 2020;Lai et al 2020;Shafeeque and Luo 2021).…”

Section: Introductionmentioning

confidence: 99%

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Spatio-temporal changes of precipitation in the Hanjiang River Basin under climate change

Jin

Chen

Zhong

et al. 2021

Theor Appl Climatol

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The Hanjiang River is the source of water for the Middle Route of the South-to-North Water Diversion Project. So a reasonable evaluation on water resources future changes in the Hanjiang River Basin (HRB) is essential to ensure its water safety. In this study, we firstly evaluated the China meteorological forcing dataset (CMFD), and then used it to measure the applicability of the eight global climate models (GCMs). Finally, the empirical orthogonal function (EOF) and clustering algorithms were used to analyze the spatial distribution status of precipitation in the HRB. The results showed that the BCC-CSM2-MR model has the best effect, followed by the IPSL-CM6A-LR model. In the SSP126, SSP245, SSP370, and SSP585 scenarios, total precipitation in the HRB all shows an increasing trend. However, the difference in the spatial distribution of precipitation intensified, for the upstream and downstream tended to be wetter, while the middle reaches had less precipitation. In the future climate scenarios, the areas with more precipitation (Class 1) increased significantly, while the areas with medium precipitation (Class 3) decreased significantly, which will enlarge the precipitation difference between regions in the HRB. The increase in the total amount of precipitation, coupled with the increase in the spatial heterogeneity of precipitation, will make the HRB more vulnerable to flooding disasters. Therefore, the water resources management measures in the HRB should be strengthened in the future.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Spatio-temporal changes of precipitation in the Hanjiang River Basin under climate change

Jin

Chen

Zhong

et al. 2021

Theor Appl Climatol

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“…A score closer to 1 indicates a better consistency between the observations and simulations, while a score of 0 indicates no match at all. focuses on the performances of the correlation coefficient and variance, according to Guo et al (2021).…”

Section: Evaluation Indexes Of the Performances Of Cmip6 Modelsmentioning

confidence: 99%

Evaluation of Events of Extreme Temperature Change between Neighboring Days in CMIP6 Models over China

Song

Yan

2021

Preprint

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In the context of global warming, the frequency and intensity of extreme weather and climate events are increasing. However, the impact of these changes that is directly felt by people is the day-to-day temperature change. Extreme temperature changes between neighboring days (ETCNs) carry substantial disease risks and socioeconomic impacts. Evaluative studies of ETCN events with global circulation models (GCMs) remain unknown in China. This study quantitatively evaluates the performances of 36 GCMs and the multi-model ensemble (MME) of the Coupled Model Intercomparison Project 6 (CMIP6) in simulating the extreme cooling (EC) and extreme warming (EW) events of two consecutive days as defined by relative thresholds. Moreover, we select the optimal models in different regions at the seasonal and annual scales in China, providing theoretical support for the frequency projection and modeling improvement of ETCN events. The results showed that from 1981 to 2013, the annual average EW events and EC events in China showed increasing but not statistically significant trends, and the frequency of EW events was higher than that of EC events. EW events mostly occurred in spring, while EC events mostly occurred in autumn. Additionally, the performances of the CMIP6 models are quite different between EC and EW events. The simulations of EC events are generally more reliable than those of EW events, and the models can also capture the annual cycle of EC events well. Furthermore, the CMIP6 models overestimate the frequencies of EW and EC events but underestimate the frequency of EC events in autumn. The CMIP6 models exhibit poor performance in simulating the trend of and interannual variability in ETCN events and can only simulate the decreasing trend in autumn. Finally, according to the overall ranking of the CMIP6 models, GFDL-ESSM4 and EC-Earth3-Veg-LR achieve the best performance in simulating EW and EC events, respectively. The CMIP6 MME only effectively improved the capabilities of the models to simulate winter EC events in the WNW region. In terms of the trend of and interannual variability in ETCN events, individual models exhibited better performances than the CMIP6 ensemble.

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Insight into historical and future spring snow cover from satellite observation and model simulations over the Northern Hemisphere

Guo

Sun²,

Meng³

et al. 2022

Preprint

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Assessment of spring snow cover fraction (SCF) can be valuable for understanding the efficacy of certain Earth system models (ESMs) in simulating the energy exchange between land and atmosphere system, global hydrological cycle and future climate impacts. In this work, we studied the model performance of 23 and 20 ESMs participating CMIP5 and CMIP6 by comparing satellite data from National Oceanic and Atmospheric Administration and National Climatic Data Center (NOAA/NCDC) over the Northern Hemisphere (NH) and its 13 sub-regions and further evaluating potential model improvement from CMIP5 to CMIP6. We found that the mean annual spring SCF that was simulated by CMIP5 and CMIP6 models was underestimated in most of the NH and overestimated on the Tibetan Plateau and in eastern Asia, and most of the model simulations showed a stronger reduction trend as well as an underestimated monthly climatological SCF. However, compared with those in CMIP5, most of the model simulations in CMIP6 had an improved ability to simulate spring SCF in terms of the annual mean, long-term trend and intra-annual variability. We also confirmed that the multi-model ensemble mean (MME) is a better way to represent the three aspects of spring SCF than most individual model simulations. The spring SCF values predicted by the CMIP5 and CMIP6 MMEs over the NH and its 13 sub-regions under different scenarios showed decreasing trends. The decreasing spring SCF trends differed under different scenarios, and the SCF under high emissions scenarios (RCP 8.5 and SSP5-8.5) continued to decrease.

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Assessment of CMIP6 in simulating precipitation over arid Central Asia

Cited by 104 publications

References 54 publications

Spatio-temporal changes of precipitation in the Hanjiang River Basin under climate change

Spatio-temporal changes of precipitation in the Hanjiang River Basin under climate change

Evaluation of Events of Extreme Temperature Change between Neighboring Days in CMIP6 Models over China

Insight into historical and future spring snow cover from satellite observation and model simulations over the Northern Hemisphere

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