Improvement of the simulation of the summer East Asian westerly jet from CMIP5 to CMIP6

Fu, Yuanhai; Lin, Zongkai; Guo, Dong

doi:10.1080/16742834.2020.1746175

Cited by 24 publications

(7 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Several studies have assessed CMIP6 by comparing the historical simulation data of CMIP5 and CMIP6 with the observed data (Gusain et al, 2020;Yuanhai et al, 2020;Zamani et al, 2020;Zhu and Yang 2020). When CMIP5 and CMIP6 are compared, the multimodel mean result (Xin et al, 2020;Yuanhai et al, 2020) rather than a specific GCM (Wyser et al, 2020) is used in most cases. Gusain et al (2020) compared CMIP6 and CMIP5 when simulating Indian summer monsoon precipitation.…”

Section: Introductionmentioning

confidence: 99%

Comparative Assessment and Future Prediction Using CMIP6 and CMIP5 for Annual Precipitation and Extreme Precipitation Simulation

Huo

Chen

et al. 2021

Front. Earth Sci.

View full text Add to dashboard Cite

This study assesses the improvement of the latest Coupled Model Intercomparison Project Phase 6 (CMIP6) over Coupled Model Intercomparison Project Phase 5 (CMIP5) for precipitation simulation. Precipitation simulations under different future climate scenarios are also compared in this work. The results show that: 1) CMIP6 has no overall advantage over CMIP5 in simulating total precipitation (PRCPTOT) and maximum consecutive dry days (CDD). The performance of CMIP6 increases or decreases regionally in PRCPTOT and consecutive dry days. But it is slightly worse than CMIP5 in simulating very wet days (R95pTOT). 2) Comparing the trend test results of CMIP5 and CMIP6 in the future, there are more areas with significant trend based on Mann–Kendall test in CMIP6 compared with that of CMIP5. The differences in PRCPTOT are mainly found in Amazon Basin and Western Africa. The differences between the R95pTOT trends mainly noticeable in South America and Western Africa, and the differences in CDD are mainly reflected in Central Asia, Sahara Desert and central South America. 3) In Southern South America and Western North America, the PRCPTOT changing rate of CMIP6 in the future under various scenarios is always greater than that of CMIP5; in Alaska, Western Africa, Southern Africa, the PRCPTOT changing rate of CMIP6 in the future under various scenarios is always less than that of CMIP5. In Southern South America, the R95pTOT changing rate of CMIP6 in the future under various scenarios is always greater than that of CMIP5; in Alaska, East Asia, North Asia, the R95pTOT changing rate of CMIP6 in the future under various scenarios is always less than that of CMIP5. In almost half of the regions, the CDD changing rate of CMIP6 is less than that of CMIP5 under all scenarios, namely Australia, Amazon Basin, Southern South America, Central America, Western North America, Central North America, Eastern North America, Central Asia, Tibet.

show abstract

Section: Introductionmentioning

confidence: 99%

Comparative Assessment and Future Prediction Using CMIP6 and CMIP5 for Annual Precipitation and Extreme Precipitation Simulation

Huo

Chen

et al. 2021

Front. Earth Sci.

View full text Add to dashboard Cite

show abstract

“…Compared with CMIP5, CMIP6 models have a certain degree of improvement and development in terms of resolution, physical parameterization, experimental design, and simulation computing capability. The simulation of the climate models from CMIP6 was closer to the observed value, while the uncertainty of simulation was smaller and the accuracy of the simulation was higher [26][27][28]. Taking the study of extreme climate indices as an example, the climate models from CMIP6 had stronger effects on the extreme temperature indices and extreme precipitation indices than the climate models from CMIP5, which can well reproduce the changing trend of the extreme climate indices [71,72].…”

Section: Discussionmentioning

confidence: 74%

“…Since the implementation of CMIP was more than 20 years, the number of participating models for CMIP6 was the largest, the scientific experiment design was the most complete, and the largest amount of model data was provided [25]. Compared with previous climate models such as CMIP5, the simulations of CMIP6 models for climate systems were closer to the observations and had less uncertainty, so the simulating ability of climate change has significantly improved [26,27]. For example, in terms of simulation for extreme climate at global scale, CMIP6 models had a general improvement in simulating the changing trend of extreme climate compared to CMIP5 models [28].…”

Section: Introductionmentioning

confidence: 99%

Future Projection for Climate Suitability of Summer Maize in the North China Plain

et al. 2022

View full text Add to dashboard Cite

Climate change has and will continue to exert significant effects on social economy, natural environment, and human life. Research on the climatic suitability of crops is critical for mitigating and adapting to the negative impacts of climate change on crop production. In the study, we developed the climate suitability model of maize and investigated the climate suitability of summer maize during the base period (1981–2010) and two future periods of 2031–2060 (2040s) and 2071–2100 (2080s) in the North China Plain (NCP) based on BCC-CSM2-MR model (BCC) from the Coupled Model Comparison Program (CMIP6) under two Shared Socioeconomic Pathways (SSP) 245 and SSP585. The phenological shift of maize under future climate scenarios was simulated by the Agricultural Production Systems Simulator (APSIM). The results showed that the root mean square errors (RMSE) between observations and projections for sunshine suitability (SS), temperature suitability (ST), precipitation suitability (SP), and integrated climate suitability (SZ) during the whole growth period were 0.069, 0.072, 0.057, and 0.040, respectively. Overall, the BCC projections for climate suitability were in suitable consistency with the observations in the NCP. During 1981–2010, the SP, ST, and SZ were high in the north of the NCP and low in the south. The SP, ST, and SZ showed a downward trend under all the future climate scenarios in most areas of NCP while the SS increased. Therein, the change range of SP and SS was 0–0.1 under all the future climate scenarios. The ST declined by 0.1–0.2 in the future except for the decrease of more than 0.3 under the SSP585 scenario in the 2080s. The decrease in SZ in the 2040s and 2080s under both SSP scenarios varied from 0 to 0.2. Moreover, the optimum area decreases greatly under future scenarios while the suitable area increases significantly. Adjusting sowing data (SD) would have essential impacts on climate suitability. To some extent, delaying SD was beneficial to improve the climate suitability of summer maize in the NCP, especially under the SSP585 scenario in the 2080s. Our findings can not only provide data support for summer maize production to adapt to climate change but also help to propose agricultural management measures to cope with future climate change.

show abstract

“…7). The BIAS is -1.0 gpm (MPI-ESM1-2-HR) to -21.3 gpm (MIROC6) at a pressure level of 850 hPa and -1.1 gpm westerlies, which was reported by Fu et al (2020). Moreover, it was reported that such underestimation exists in both CMIP5 and CMIP6 models (Fu et al 2020).…”

Section: Geopotential Heightmentioning

confidence: 78%

Evaluation of the Performance of CMIP6 Model Simulations for the Asian-Pacific Region: Perspectives from Multiple Dimensions

Song

Zhang

Yan

2022

Preprint

View full text Add to dashboard Cite

A comprehensive climate model assessment from multiple dimensions is critical for model selection to reduce uncertainties. Here, we evaluated the performance of seven models involved in the Coupled Model Intercomparison Project-6 (CMIP6) by comparing the simulated meteorological variables at the near-surface (sea ice cover and sea surface temperature) and pressure levels (air temperature, specific humidity, zonal wind, meridional wind, and geopotential height at 850 hPa, 500 hPa, and 200 hPa) to those using ERA5 reanalysis data for the 1995-2014 period from the perspectives of climatological and interannual variability. Then, a comprehensive rating index approach was applied to rank the models. The results show that the CMIP6 models could mostly reproduce the spatial variability of the climatology. However, there were also systematic biases. The sea ice cover and sea surface temperature exhibited noticeable biases of approximately -13.2% and 0.6 °C, respectively. Additionally, all models underestimated air temperature and geopotential height, while overestimating specific humidity in the middle troposphere and zonal and meridional wind speeds in the upper troposphere. Regarding interannual variability, the CMIP6 models performed well for the variables at a pressure level of 850 hPa and with sea ice cover. Taken together, all variables showed that NorESM2-MM exhibited good performance in terms of climatology, while MPI-ESM1-2-LR exhibited good interannual variability. Overall, in regard to the comprehensive rate index, MPI-ESM1-2-LR performed the best among the seven models. This study provides valuable scientific references for selecting the available CMIP6 models as lateral boundary conditions towards dynamical downscaling over Asian-Pacific area.

show abstract

Improvement of the simulation of the summer East Asian westerly jet from CMIP5 to CMIP6

Cited by 24 publications

References 22 publications

Comparative Assessment and Future Prediction Using CMIP6 and CMIP5 for Annual Precipitation and Extreme Precipitation Simulation

Comparative Assessment and Future Prediction Using CMIP6 and CMIP5 for Annual Precipitation and Extreme Precipitation Simulation

Future Projection for Climate Suitability of Summer Maize in the North China Plain

Evaluation of the Performance of CMIP6 Model Simulations for the Asian-Pacific Region: Perspectives from Multiple Dimensions

Contact Info

Product

Resources

About