The impact of urbanization on temperature trends in China was investigated with emphasis on two aspects of urbanization, land cover change, and human activity. A new station classification scheme was developed to incorporate these two aspects by utilizing land cover and energy consumption data. Observation temperature data of 274 stations and National Centers for Environmental Prediction (NCEP)–National Center for Atmospheric Research (NCAR) reanalysis temperature from 1979 to 2010 were used in conducting the observation minus reanalysis (OMR) method to detect urban influence. Results indicated that nearly half of the stations in the study area have been converted from nonurban to urban stations as a result of land cover change associated with urban expansion. It was determined that both land cover change and human activity play important roles in temperature change and contribute to the observed warming, particularly in urbanized stations, where the highest amount of warming was detected. Urbanized stations showed higher OMR temperature trends than those of unchanged stations. In addition, a statistically significant positive relationship was detected between human activity and temperature trends, which suggests that the observed warming is closely related to the intensity and spatial extent of human activity. In fact, the urbanization effect is strongly affected by specific characteristics of urbanization in local and regional scales.
[1] Human activity is an important contributor to local temperature change, especially in urban areas. Energy consumption is treated here as an index of the intensity of human induced local thermal forcing. The relationship between energy consumption and temperature change is analyzed in China by Observation Minus Reanalysis (OMR) method. Temperature trends for observation, reanalysis and OMR are estimated from meteorological records and 2 m-temperature from NCEP/NCAR Reanalysis 1 for the period 1979-2007. A spatial mapping scheme based on the spatial and temporal relationship between energy consumption and Gross Domestic Production (GDP) is developed to derive the spatial distribution of energy consumption of China in 2003. A positive relationship between energy consumption and OMR trends is found in high and mid energy consumption region. OMR trends decline with the decreasing intensity of human activity from 0.20 C/decade in high energy consumption region to 0.13 C/decade in mid energy consumption region. Forty-four stations in high energy consumption region that are exposed to the largest human impact are selected to investigate the impact of energy consumption spatial pattern on temperature change. Results show human impact on temperature trends is highly dependent on spatial pattern of energy consumption. OMR trends decline from energy consumption center to surrounding areas (0.26 to 0.04 C/decade) and get strengthened as the spatial extent of high energy consumption area expands (0.14 to 0.25 C/decade).Citation: Li, Y., and X. Zhao (2012), An empirical study of the impact of human activity on long-term temperature change in China: A perspective from energy consumption,
A set of daily precipitation data from 1958 to 2007 was analysed for the area in and around the Three Gorges Reservoir Region. Annual and monthly precipitation, number of rainy days (NRDs), precipitation intensity (INT), and seven indices of extreme precipitation and drought were examined. Correlation between circulation indices and climate parameters was analysed. Significant decreases were detected in spring, fall, winter and annual NRDs, while significant increases were found in precipitation INT. Inter-decadal changes are obvious, it was wettest in the early 1980s, and dry in the first few years and after 1990. No significant changes are found in extreme precipitation/drought events, but a closer examination suggested they might become more frequent after 1980. Precipitation totals and days of extreme precipitation in a specific period were found to be negatively correlated with western North Pacific Monsoon Index (WNPMI); positively correlated with summer average of index of the ridge of western Pacific subtropical high; and positively correlated with summer average of index of INT of western Pacific subtropical high and the winter-summer difference of Tibetan Plateau (TP) index. These links were significant but not strong, which might manifest the characteristics of Three Gorges Area as a transition zone, where precipitation is influenced by multiple systems. Regional management should be more careful to adjust to these changes.
Climate change in the Pearl River Delta (PRD) has attracted growing attention along with rapid urbanization in Southern China. Annual mean temperatures in this area have increased more rapidly than the average level, which can be attributed to population expansion and land use changes in this region.In this study, temperature records from 31 weather stations in the PRD in Guangdong, China and the global dataset from the National Centres for Environmental Prediction (NCEP) Reanalysis (R-2) are analysed. Data from NCEP R-2 and temperature soundings taken at 850 hPa are used to define the background temperature. Anthropogenic temperature is then calculated according to the observed temperature and background temperature. The relationships of anthropogenic temperature to population density and area ratio of land-use types are analysed by univariate and multiple regression analysis techniques.Spatial distribution of anthropogenic temperature in the daytime is different from that at night. Model results indicate that relationships between population and anthropogenic temperature in the daytime are logarithmic or inverse but tend to be linearly related at night. Multiple regression analysis conducted on the area ratios of land-use types and anthropogenic temperature shows that a strong relationship exists between the two in spring and autumn. Positive correlations with anthropogenic temperature from arid land, water bodies and urban land, as well as a negative correlation from woodland, are detected regardless of time of day. Contrary to the paddy field, grassland and sea show a negative correlation with anthropogenic temperature in the daytime and a positive correlation at night.
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