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Rice is a major staple food grain for more than half of the world's population, and China is the largest rice producer and consumer in the world. In a climate-warming context, the frequency, duration and intensity of heat waves tend to increase, and rice production will be exposed to higher heat damage risks. Understanding the negative impacts of climate change on the rice supply is a critical issue. In this study, a new perspective on agricultural weather index insurance is proposed to investigate the impact of extreme high-temperature events on rice production in South China in the context of climate change. Based on data from meteorological stations in Anhui Province in China from 1961 to 2018 and the projected data from five Global Climate Models under three representative concentration pathway (RCP) scenarios from 2021 to 2099, the spatial–temporal characteristics of heat stress and its influence on rice production were analyzed by employing a weather index insurance model. The interdecadal breakpoints in the trends of the heat stress weather insurance index (HSWI) and the payout from 1961 to 2018 in 1987 were both determined, which are consistent with the more significant global warming since the 1980s. The largest increase after 1987 was found in the southeastern part of the study area. The projected HSWI and the payout increased significantly from 2021 to 2099, and their growth was faster with higher radiative forcing levels. The HSWI values were on average 1.4 times, 3.3 times and 6.1 times higher and the payouts were on average 3.9 times, 9.8 times and 15.0 times higher than the reference values for the near future, mid-future and far future, respectively. The results suggest that a more severe influence of heat damage on rice production will probably happen in the future, and it is vital to develop relevant adaptation strategies for the effects of a warmer climate and heat stress on rice production. This paper provides an alternative way to transform the evaluation of the extreme climate event index into the quantitative estimation of disaster impacts on crop production. [ABSTRACT FROM AUTHOR]

The arid region of northwest China (ARNC) is one of the most sensitive areas to global warming. However, the performance of new Global Climate Models (GCMs) from phase 6 of the Coupled Model Intercomparison Project (CMIP6) in simulating climate in this region, especially in the subregions, is not clear yet. Based on the temperature dataset from historical runs of CMIP6, this paper analyzed and evaluated the simulation ability of 29 GCMs in reproducing the annual mean temperature (tas), annual mean maximum temperature (tasmax) and annual mean minimum temperature (tasmin) in the ARNC and subregions from 1961 to 2014. The results show that (1) the correlation coefficients (CCs) between simulation and observation time series for the mean of two model ensembles (MME for equal-weight multi-model ensemble and PME for preferred-model ensemble) are generally better than those of 29 individual GCMs, with CCs ranging from 0.38 to 0.87 (p < 0.01). (2) All the models can simulate the significant warming trend of the three temperature elements in the study area well. However, the warming magnitude simulated by most of the models (41%) is smaller than the observations except for tasmax, which is also shown in the MME. (3) The spatial pattern of the three temperature elements can be better reflected by most models. Model simulation ability for the ARNC is better compared to that of the four subregions, with a spatial CC greater than 0.7 (p < 0.01). Among the subregions, the simulation performance of the north of Xinjiang for spatial pattern is superior to that of the other regions. (4) The preferred models for each subregion are various and should be treated differently when used. Overall, the PME outperforms both the MME and the individual models; it can not only simulate the linear trend accurately but also reduce the deviation effectively. [ABSTRACT FROM AUTHOR]

The western Tianshan Mountains region in China has a complex topography where basins, mountains and glaciers co-exist. It is of great significance to study the sensitivity of meteorological factors in this region to different parameterization schemes of climate models. In this paper, the regional climate model RegCM4.5 is used to simulate the meteorological factor (mean temperature, maximum temperature, minimum temperature, precipitation and wind speed) occurring in the western Tianshan Mountains region from 2012 to 2016, so as to investigate the effects of different cumulus convective schemes (Grell, Tiedtke and Emanuel), including land cumulus convective schemes (LCCs) and ocean convective schemes (OCCs) on annual and seasonal simulations of meteorological factor by using the schemes of RUN1 (Grell for LCC and Tiedtke for OCC), RUN2 (Tiedtke for LCC and Emanuel for OCC), RUN3 (Grell for LCC and Emanuel for OCC) and ENS (the ensemble of RUN1, RUN2 and RUN3). The results show that the simulations of annual and seasonal meteorological factors are not significantly sensitive to the combination of LCCs and OCCs. In the annual simulations, RUN2 scheme has the best simulation performance for the maximum, average and minimum temperatures. However, other schemes of precipitation simulation outperform RUN2 scheme, and there is no difference among the four schemes for wind speed simulation. In the seasonal simulations, RUN2 scheme still performs well in the simulation of the average, maximum and minimum temperatures for four seasons, except for the simulation of the average temperature in spring and summer. For the simulation of the maximum temperature in summer, RUN2 scheme performs the same as ENS. For the simulation of other seasons, different meteorological factors have different performances in four seasons. Overall, the results show that different combinations of cumulus convection schemes can improve the simulation performance of meteorological factors in the western Tianshan Mountains of Xinjiang. [ABSTRACT FROM AUTHOR]

Linking sinuosity, a fairly recently developed metric, with high temperature extremes (HTEs) can be both useful and insightful to better understand the physical mechanisms behind HTEs. However, it is not clear whether there exists a relationship between the sinuosity changes and HTE changes in present and future climate conditions over southeastern China. In this paper, the anomalous characteristics of the atmospheric circulation are quantified by sinuosity. Three sinuosity metrics are used in this study: individual sinuosity (SIN), aggregate sinuosity (ASIN), and comprehensive sinuosity (CSIN). Furthermore, we examine the relationship between sinuosity changes and HTE changes in present and future climate conditions. ASIN is strongly correlated with surface air temperature (SAT). We find that the influence of individual sinuosity (SIN) at different latitudes on the SAT of southeastern China is different. The SIN of low (middle) latitude isohypses has significant positive (negative) correlations with the SAT of southeastern China. The SIN of high-latitude isohypses is rather limited and can therefore be ignored. The projected relationship between the sinuosity changes and HTE changes in the late 21st century suggests similar results. The change in SAT is related to the changes in climate variables over southeastern China in the future, and these changes increase with the increase in Z500 or V850 and the decrease in U500. Moreover, the frequencies of large (small) comprehensive sinuosity (CSIN) values at low (mid) latitudes will increase. At the end of the 21st century, Z500 isohypses at different latitudes will have an obvious poleward shift. Our results indicate that measuring the aggregate waviness of the midtropospheric flow (via sinuosity) can provide insight regarding HTEs, and the climate model output can be used to examine the future likelihood of increased HTE. [ABSTRACT FROM AUTHOR]

Extreme precipitation has often occurred in Southeastern China, while the possible mechanism is not clear. In order to bridge the scale gap between large-scale circulation and extreme precipitation, in this paper, the k-means clustering technique—a common method of weather-type (WT) analysis—was applied to regional 850-hPa wind fields. The reasonable determination of k values can make the later WT analyses more reliable. Thus, the Davies–Bouldin (BD) criterion index is used in the clustering process, and the optimal value of the k was determined. Then, we obtain and analyze the frequency, persistence, and progression of WTs. The rule of transitions from one WT to another may help explain some of the physical processes in winter. We found a special evolutionary chain (WT3→WT1→WT2→WT5→WT3) that can be used to explain the cold wave weather process in winter. Different WTs in the evolutionary chain correspond well to different stages of the cold wave weather process (gestation (WT3), outbreak (WT1), eastward withdrawal (WT2), and extinction (WT5)). In addition, we found that there are obvious differences in precipitation between December and February. After reassembling five kinds of WTs, two modes are formed: dry WTs and wet WTs. Our research shows that the intraseasonal variation of precipitation can be attributed to the fluctuation between the wet and dry WTs, and the different phases of teleconnection can correspond well with it. For example, the relative frequencies of wet WTs are higher in February. These WTs correspond to the positive phase of the WP and ENSO, the negative phase of the EA and EU, and the strong MJO state of the second, third, and eighth phase. Our work has well established the relationship between synoptic scale and large-scale circulation, which provides a reference for climate model simulation and future climate prediction. [ABSTRACT FROM AUTHOR]