The dry-wet climate regionalization index, potential evapotranspiration calculation methods, standard and named method of the dry-wet climate regionalization, etc., are reviewed in this paper since the middle of the 20th century. Using the arid index, climate regionalization and class standard were given on the basis of former review. Then, the characteristics of dry-wet climate division were analyzed using observed data at 2207 national meteorological stations from 1981 to 2010. The results showed that arid area (including the extreme arid, arid and semi-arid areas) was 4.692 million hm2 in China, accounting for 48.8% of the land area in China, which included 0.878 million hm2 (9.1%), 2.092 million hm2 (21.8%) and 1.722 million hm2 (17.9%) for extreme arid, arid and semi-arid area respectively. The arid areas were mainly distributed in Xinjiang, Inner Mongolia, Tibet, Qinghai, Gansu province and other western regions. The sub-humid area, wet area and excessive wet area accounted for 16.2%, 27.8% and 8.8% of China’s land area respectively, mainly located in the south of the Yangtze River and Northeast China.
The performance of 4 ACCMIP models (GFDL-AM3, NCAR-CAM3.5, GISS-E2-R and MIROC-CHEM) were evaluated in simulating surface PM2.5 concentration by using observation data, which obtained from MODIS and MISR satellite data. Specifically, the performance of reproducing the spatial distribution and time evolution were evaluated in this study. As shown from the results, ACCMIP models can reproduce the spatial pattern of surface PM2.5 concentration well over eastern China. The performance of GFDL-AM3 in simulating surface PM2.5 spatial pattern is the best, among the four models. The greater inconsistency was found in central Xinjiang and western Inner Mongolia. There is a good agreement in time series between the observational data and the model data. Multimodel ensemble mean can simulate the trends of surface PM2.5 concentration in Northeast China, Central China, coastal region of East China and western Xinjiang.
The performance of six Chinese climate models in simulating Arctic sea-ice in CMIP5 are revisited to investigate their uncertainty in future climate projections. We re-estimate all CMIP5 models using two indicators and suggest six “good” (G6) out of 40 models in reproducing Arctic sea-ice: CESM1-BGC, HadGEM2-CC, IPSL-CM5A-LR, MPI-ESM-LR, MPI-ESM-MR, MRI-CGCM3. However, it is found that Chinese models cannot well reproduce observed sea-ice extent in the past decades (BCC-CSM1-1, BCC-CSM1-1-m, FGOALS-g2) and also fail to predict Arctic climate compared to the multi-model ensemble (MME) mean around 2050s. FGOALS-g2 (BCC-CSM1-1, BCC-CSM1-1-m) significantly overestimated (underestimated) the current extent of Arctic sea-ice and present a larger (smaller) sea-ice extent than MME mean in the future. FIO-ESM well estimated the current Arctic sea-ice extent but fail in predicting it in future, with a huge bias of the sensitivity to the warming surface. Relatively, BNU-ESM and FGOALS-s2 successfully reproduce the current Arctic sea-ice extent and predict an Arctic climate close to CMIP5 MME mean in 2050s. BNU-ESM, with the smallest bias in simulating Arctic sea-ice, is considered as the best one among the six Chinese models. Furthermore, we would expect a significant improvement of Chinese climate models in simulating Arctic sea-ice in the next phase of CMIP experiments, so that smaller biases in projecting not only local climate but also remote responses that close to CMIP MME mean projections could be expected.
The ability of German regional climate model (REMO) to simulate the near surface air temperature and total precipitation over China in 1989-2008 were assessed with the use of Taylor diagrams and bias analysis. Comparing the simulated near surface air temperature with a 20-year observational dataset from China, the spatial correlation coefficient was relatively high (0.94). However, the spatial correlation coefficient for total precipitation was relatively low (0.42). The near surface air temperature simulated by REMO was higher than the observed values in most part of China, showing a bias range within ±4℃. Significant cold bias of about -4℃ to -2℃occurred over most of the Qinghai-Tibetan Plateau. In terms of total precipitation, the simulated values were higher than the observed ones, with biases evenly distributed. The annual mean bias in most part of China was within ±300 mm. Except for the Qinghai-Tibetan Plateau, South China and Southwest China, REMO accurately reflected the distribution of near surface air temperature and total precipitation. REMO represented the temperature and total precipitation well in North China and Northeast China. REMO simulations were quite close to observations for near surface air temperature in summer and total precipitation in winter. REMO still needs to be improved in complex terrain areas.
Simulations of China climate and its decadal changes are compared between Historical experiment and Decadal experiment of Beijing Climate System Model version 1.1 (BCC_CSM1.1) submitted to CMIP5. Results show that the climatological distribution of the precipitation in China is better simulated by the Decadal experiment. The error of decadal anoamlies in China simulated by the Decadal experiment is less than the Historical experiment. As for the decadal change of the East China precipitation in the late of the 1970s, the Decadal experiment can simulate the increased precipitation in the Yangtze River basin, while the Historical experiment produce the opposite feature with the observation. The main difference between Decadal experiment and Historical experiment is that the first one initializes the observed sea surface temperature. In order to discuss the importance of simulated sea surface temperature (SST) by the coupled climate model, the Nudging experiment (the modeled SST is nudged to the observed SST during the simulation) and the Decadal experiment were compared. It is found that the Nudging experiment can significantly simulate the “North drought and South Flood” feature in East China. Moreover, it can reproduce the stronger and southward movement of the East Asian jet stream. Those comparisons indicate that the hindcast of the SST by coupled models may have important influence on model’s capability in prediction of East Asian climate and its decadal changes.
Based on data of temperature, precipitation, relative humidity, sunshine hours, and maximum snow depth during 1961-2013 from 60 meteorological stations located in Tianshan mountainous area, this paper analyzed the change of climatic factors above using time series analysis and spatial analysis method. Path analysis was used to explore the impact of temperature, precipitation, relative humidity and sunshine hours on snow depth. The results showed that the climate in Tianshan mountainous area had changed a lot significantly during 1961-2013. Its main features included the significant increase of temperature and precipitation, the local increase or decrease of relative humidity, local diming for the decrease of sunshine hours and local increase of maximum snow depth. To some degree, climate warming can reduce the assurance rate of snowfall in Tianshan mountainous area. Temperature, precipitation and sunshine hours impacted on snow depth by 9 influence paths including 3 direct influence paths and 6 indirect influence paths. Direct influence paths of 3 were from temperature, precipitation and sunshine hours to snow depth directly, respectively. And 6 indirect influence paths were from temperature to precipitation to snow depth, from temperature to sunshine hours to snow depth, from precipitation to temperature to snow depth, from precipitation to sunshine hours to snow depth, from sunshine hours to temperature to snow depth and from sunshine hours to precipitation to snow depth, respectively. It was showed that the overall effect of temperature on snow was far greater than the influence of precipitation and sunshine hours.
The structure of big data in global climate change science was discussed in terms of climate system observations, numerical simulation data, economic and social data, and land use data. Processing methods of big data were analyzed from perspectives of data integration, storage, sharing, digital simulation, and data mining. The application value of big data in the Global Framework for Climate Services and Future Plans for Earth, and the outlook of big data’s future development were discussed also.
The spatial and temporal variation of urban heat island in summer in Chengdu have been analyzed by using MODIS land surface temperature and DMSP/OLS nighttime light imagery data. The results showed that along with the urbanization process, great changes have taken place in the thermal environment. The temperature field transferred each other and the medium temperature was mainly replaced by the sub-high temperature in the whole area. The urban heat island has a large diurnal variation, the heat island area is increasing during summer daytime in Chengdu, resulting in Chengdu and its surrounding satellite towns integral heat island become a major heat island area. The contribution to regional warming of the urban heat island in 2000 and 2010 were 0.13℃ and 0.29℃ respectively, and the variation reaches 0.16℃. However, there is no existing of a large area of strong heat island in night. Since 2000, the size of the urban heat island in the old city area showed an increasing trend, but the change is not obvious. The scale of urban expansion district heat island had significantly increased, the newly enhanced heat island area reached 166.43 km2 and changed in 54% in 2010 comparing with 2000. The decrease of daily temperature range in Chengdu which has a relatively high level of urbanization is completely attributable to the urbanization effect, indicating that changes in surface temperature and urbanization are closely related. The urban heat island and the square root of the population have a good positive correlation. In the non-agricultural population of Chengdu, every increase of 1 million persons is linked to an increase of 0.4℃ of the heat island intensity.
Global warming caused by human activities has become a significant threat to the safety of living conditions. Economic structural feature has been proved to be key influencing factor on greenhouse gas. In order to explore more information, the internal structure of manufacturing sector and service sector are selected as the main impacting factors on carbon emissions. Based on the data from the major members of OECD countries, we conducted an econometric analysis on the carbon emissions controlled by the upgrading of internal structures of manufacturing and service. The main conclusions are as followed: first, the internal structure is much more informative than the traditional economic structure, and is able to provide a better standard to determine the specific period of regional development; second, the carbon emission peak would lag to the peak of regional industrialization; third, the trend of carbon emissions caused by human activities through the economic developing shows an inverted-U curve, which is an objective and inherent changing law. Therefore, countries that have not reached the top of industrialization have reasons to strive for a higher carbon quota; meanwhile, every region should promote the carbon mitigation through the upgrading of the internal industrial structures.
This study analyzed the mechanism of how urbanization affects carbon emissions from the three aspects of scale, structure and efficiency in theory. Then the 1978-2012 data are used to analyze the influence of the urbanization on carbon emissions from the aspects of scale effect, structure effect and technology effect by the method of Logarithmic Mean Divisia Index (LMDI). According to the LMDI decomposition results, economic growth is the main factor of increasing per capita CO2 emissions; and structural adjustment in the process of urbanization is the main factor of decreasing per capita CO2 emissions; technology effect in the process of urbanization is lowering per capita CO2 emissions, but a less extent compared with structure effect. Also, the results suggest that effects of changes in the structure of industry are becoming more and more important, and if China wants to achieve a low-carbon urbanization, there needs to be fully tap to play a technical effect by improving energy efficiency and emission reduction ability.
Carbon Capture, Utilization, and Storage (CCUS) was widely acknowledged as one of the most important technologies that can reduce CO2 emissions in large scale. In order to promote the development and application of CCUS, the major developed economies, such as EU, UK, and USA, have been actively advocating the institutionalization and standardization of CCUS implementation. This paper summarizes the CCUS relevant international regulations, systematically reviews the policies and legislations of advanced developed countries, and analyzes the legal system and CCUS regulation situation of China. By comparing the domestic CCUS policy environment of developed countries, it is showing that there is a relatively complicated process for China to establish the special legislations and policies of CCUS. By using the experience of developed countries and regions for reference, China should build the CCUS public law system focusing on the definition of CO2, the identification of CCUS project jurisdiction, the division of the ownership and responsibility, intellectual property rights transfer and protection, the establishment of uniform technical standers and related incentive policies, and the establishment of systematic legal framework. The improvement of CCUS policy and legislation environment will effectively promote the healthy development of CCUS in China.