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Concurrent compound dry and hot events (CDHEs) amplified more damange on the ecosystems and human society than individual extremes. Under climate change, compound dry and hot events become more frequent on a global scale. This paper proposes a mathematical method to quantitatively attribute changes of CDHEs to changes of precipitation, change in temperature and change in the dependence between precipitation and temperature. The attribution is achieved by formulating the total differential equation of the return period of CDHEs among Meta-gaussian model. A case study of China is devised based on monthly precipitation and temperature data during the period from 1921 to 2020 for 80 major river basins. It is found that temperature is the main driving factor of increases in CDHEs for 49 major river basins in China, except for the upper and middle reaches of the Yangtze River. In West China, precipitation changes drove the increase in CDHEs in 18 river basins (23%), particularly in parts of North Xinjiang, Qinghai and Gansu. On the other hand, dependence between precipitation and temperature dominated changes of CDHEs in 13 river basins (16%) of China with other factors, including parts of South China, East China and Northwestern China. Furthermore, changes in both the mean and spread of precipitation and temperature can also contribute to changes in CDHEs. [ABSTRACT FROM AUTHOR]

Since climatic condition is the important foundation for human subsistence and development and the key factor in sustainable development of economy and society, climate change has been a global issue attracting great attentions of politicians, scientists, governments, and the public alike throughout the world. Existing climate regionalization in China aims to characterize the regional differences in climate based on years of the mean value of different climate indexes. However, with the accelerating climate change nowadays, existing climate regionalization cannot represent the regional difference of climate change, nor can it reflect the disasters and environmental risks incurred from climate changes. This paper utilizes the tendency value and fluctuation value of temperature and precipitation from 1961 to 2010 to identify the climate change quantitatively, and completes the climate change regionalization in China (1961-2010) with county administrative regionalization as the unit in combination with China's terrain feature. Level-I regionalization divides China's climate change (1961-2010) into five tendency zones based on the tendency of temperature and precipitation, which are respectively Northeast China-North China warm-dry trend zone, East China-Central China wet-warm trend zone, Southwest China-South China dry-warm trend zone, Southeast Tibet-Southwest China wet-warm trend zone, and Northwest China-Qinghai-Tibet Plateau warm-wet trend zone; level-II regionalization refers to fourteen fluctuation regions based on level-I regionalization according to the fluctuation of temperature and precipitation. [ABSTRACT FROM AUTHOR]

The spatial and temporal variations of the vegetation carbon flux in China from 1901 to 2005 were studied using the vegetation net primary production (NPP) values from seven Earth-system models. In addition, the temporal and spatial changes in the nitrogen deposition flux in China were studied using the NHx and NOx fluxes from 1901 to 2005. The relationship between changes in the carbon flux, nitrogen flux and climate was analyzed. The results show that (1) over the past 100 years, NPP in China has shown an upward trend. The average trend coefficient is 0.88 and the NPP distribution trend is generally low in the north and high in the south, with a gradual increase from the northwest to the southeast. Temperature, precipitation and radiation are all conducive to plant growth in the direction of the gradient. The correlation coefficients between the ensemble model mean NPP and temperature, precipitation, longwave radiation and shortwave radiation are 0.88,0.73,0.91 and 0.67, respectively. (2) In the past 100 years, the NHx and NOy fluxes in China have shown an upward trend, with trend coefficients of 0.98 and 0.98, respectively, which pass the 99.9% confidence level of the t-test. NHx and NOy fluxes are also generally low in the north and high in the south, with a gradual increase from the northwest to the southeast in a step-like pattern. (3) The spatial distribution of the correlation coefficients between the NHx and NOy fluxes and air temperature is similar, with only slight differences in values. The spatial distribution of the correlation coefficients between the NHx and NOy fluxes and precipitation is similar in overall pattern, but the pattern is relatively complicated, with a positive-negative-positive-negative-positive pattern occurring across the monsoon region from north to south, and a negative-positive-negative-positive pattern occurring beyond the monsoon region from east to west. (4) The spatial distribution of the correlation coefficients between the NHx and NOy fluxes and NPP shows a generally consistent pattern, but the pattern is relatively uneven. The average distribution of the ensemble model mean is positive correlation in northeast China and southwest China, and alternating positive and negative correlation in other regions. [ABSTRACT FROM AUTHOR]

Land cover is one of the most basic input elements of land surface and climate models. Currently, the direct and indirect effects of land cover data on climate and climate change are receiving increasing attentions. In this study, a high resolution (30 m) global land cover dataset (GlobeLand30) produced by Chinese scientists was, for the first time, used in the Beijing Climate Center Climate System Model (BCC_CSM) to assess the influences of land cover dataset on land surface and climate simulations. A two-step strategy was designed to use the GlobeLand30 data in the model. First, the GlobeLand30 data were merged with other satellite remote sensing and climate datasets to regenerate plant functional type (PFT) data fitted for the BCC_CSM. Second, the up-scaling based on an area-weighted approach was used to aggregate the fine-resolution GlobeLand30 land cover type and area percentage with the coarser model grid resolutions globally. The GlobeLand30-based and the BCC_CSM-based land cover data had generally consistent spatial distribution features, but there were some differences between them. The simulation results of the different land cover type dataset change experiments showed that effects of the new PFT data were larger than those of the new glaciers and water bodies (lakes and wetlands). The maximum value was attained when dataset of all land cover types were changed. The positive bias of precipitation in the mid-high latitude of the northern hemisphere and the negative bias in the Amazon, as well as the negative bias of air temperature in part of the southern hemisphere, were reduced when the GlobeLand30-based data were used in the BCC_CSM atmosphere model. The results suggest that the GlobeLand30 data are suitable for use in the BCC_CSM component models and can improve the performance of the land and atmosphere simulations. [ABSTRACT FROM AUTHOR]