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Analyses of the variation characteristics for aridity index (AI) can further enhance the understanding of climate change and have effect on hydrology and agriculture. In this paper, based on the data of 283 standard meteorological stations, the temporal-spatial variations and the influences of climate factors on AI were investigated and the relationship between AI and two climate indices (the Arctic Oscillation (AO); El Nino-Southern Oscillation (ENSO)) were also assessed in northern China (NC) during the period from 1961 to 2012. The results revealed that the annual mean AI decreased at the rate of −0.031 per decade in the past 52 years and the trend was statistically significant at the 0.01 level. The Mann-Kendall (M-K) test presented that the percentages of stations with positive trends and negative trends for AI were 10 and 81.9 % (22.6 % statistically significant), respectively. Spatially, in the western part of 100° E, the extremely dry area declined and the climate tended to become wet obviously. In the eastern part of 100° E, dry area moved toward the east and the south, which resulted in the enhancement of semiarid area and the shrinkage of subhumid area. The contributions of sunshine duration and precipitation to the decline of AI are more than those of other meteorological variables in NC. Moreover, the average temperature has risen significantly and AI decreased in NC, which indicated the existence of 'paradox.' Relationship between climate indices (AO and ENSO) and AI demonstrated that the influence of ENSO on AI overweight the AO on AI in NC. [ABSTRACT FROM AUTHOR]

The parameterization of physical processes is one of the critical elements to properly simulate the regional climate over eastern China. It is essential to conduct detailed analyses on the effect of physical parameterization schemes on regional climate simulation, to provide more reliable regional climate change information. In this paper, we evaluate the 25-year (1983-2007) summer monsoon climate characteristics of precipitation and surface air temperature by using the regional spectral model (RSM) with different physical schemes. The ensemble results using the reliability ensemble averaging (REA) method are also assessed. The result shows that the RSM model has the capacity to reproduce the spatial patterns, the variations, and the temporal tendency of surface air temperature and precipitation over eastern China. And it tends to predict better climatology characteristics over the Yangtze River basin and the South China. The impact of different physical schemes on RSM simulations is also investigated. Generally, the CLD3 cloud water prediction scheme tends to produce larger precipitation because of its overestimation of the low-level moisture. The systematic biases derived from the KF2 cumulus scheme are larger than those from the RAS scheme. The scale-selective bias correction (SSBC) method improves the simulation of the temporal and spatial characteristics of surface air temperature and precipitation and advances the circulation simulation capacity. The REA ensemble results show significant improvement in simulating temperature and precipitation distribution, which have much higher correlation coefficient and lower root mean square error. The REA result of selected experiments is better than that of nonselected experiments, indicating the necessity of choosing better ensemble samples for ensemble. [ABSTRACT FROM AUTHOR]

In this study, a stepwise-clustered statistical downscaling model is established to simulate future apparent temperatures based on NCEP reanalysis data and four global climate models (GCMs). AP is a metric used to quantify thermal comfort or discomfort. The model can express nonlinear relationships between variables at large scale and local scale. The model is employed for projecting future apparent temperature changes over the Pearl River Delta (PRD), on the south coast of China, under three representative concentration pathways (RCP) scenarios. The cluster tree generated for the daily apparent temperature is calibrated for the period 1971–1990 and validated for the period 1991–2000. The R2 values obtained for the validation period at eight selected cities for four GCMs (i.e., CanESM2, CNRM-CM5, CSIRO-Mk3-6-0, and IPSL-CM5A-LR) are 0.88, 0.87, 0.86, and 0.87, respectively. The results reflected that apparent temperature is projected to have a constant increment over the PRD in the future period (2035–2095). Moreover, the monthly apparent temperature in April has the largest expected increment in the future period, while the smallest increment is found in January. The results also indicated that the apparent temperature increases faster than the air temperature under the RCP4.5 and the RCP8.5 scenarios in the PRD. The findings illuminate that the expected increase in apparent temperature over the PRD can be mainly explained by increasing air temperatures and decreasing wind speeds. The results can provide decision makers with useful information for urban health risk assessments. [ABSTRACT FROM AUTHOR]

Gridded climatic datasets with fine spatial resolution can potentially be used to depict the climatic characteristics across the complex topography of China. In this study, we collected records of monthly temperature at 1153 stations and precipitation at 1202 stations in China and neighboring countries to construct a monthly climate dataset in China with a 0.025° resolution (~ 2.5 km). The dataset, named LZU0025, was designed by Lanzhou University and used a partial thin plate smoothing method embedded in the ANUSPLIN software. The accuracy of LZU0025 was evaluated based on three aspects: (1) Diagnostic statistics from the surface fitting model during 1951–2011. The results indicate a low mean square root of generalized cross validation (RTGCV) for the monthly air temperature surface (1.06 °C) and monthly precipitation surface (1.97 mm1/2). The method used variable square root transformation for the spline surface fitting to reduce positive skewness in the measured precipitation values and no variable transformation in air temperature case. This indicates that the surface fitting models are accurate. (2) Error statistics of comparisons between interpolated monthly LZU0025 with the withholding of climatic data from 265 stations during 1951–2011. The results show that the predicted values closely tracked the real true values with values of mean absolute error (MAE) of 0.59 °C and 70.5 mm and standard deviation of the mean error (STD) of 1.27 °C and 122.6 mm. In addition, the monthly STDs exhibited a consistent pattern of variation with RTGCV. (3) Comparison with other datasets. This was done in two ways. The first was via comparison of standard deviation, mean, and time trend derived from all datasets to a reference dataset released by the China Meteorological Administration (CMA), using Taylor diagrams. The second was to compare LZU0025 with the station dataset in the Tibetan Plateau. Taylor diagrams show that the standard deviation derived from LZU had a higher correlation with that produced by the CMA (R = 0.76 for air temperature, and R = 0.96 for precipitation) compared to those from other datasets. The standard deviation for the index derived from LZU was more close to that induced from CMA, and the centered normalized root-mean-square difference for this index derived from LZU and CMA was lower. A similar superior performance of LZU was found in the comparison of mean and time trend derived from LZU and those from other datasets. LZU0025 had high correlation with the Coordinated Energy and Water Cycle Observation Project (CEOP)—Asian Monsoon Project (CAMP) Tibet surface meteorology station dataset for air temperature, despite a non-significant correlation for precipitation at a few stations. Based on this comprehensive analysis, we conclude that LZU0025 is a reliable dataset. LZU0025, which has a fine resolution, can be used to identify a greater number of climate types, such as tundra and subpolar continental, along the Himalayan Mountain. We anticipate that LZU0025 can be used for the monitoring of regional climate change and precision agriculture modulation under global climate change. [ABSTRACT FROM AUTHOR]

This study reveals the impacts of climatic variable trends on drought severity in Xinjiang, China. Four drought indices, including the self-calibrating Palmer drought severity index (sc-PDSI), Erinç's index ( I ), Sahin's index ( I ), and UNEP aridity index (AI), were used to compare drought severity. The ensemble empirical mode decomposition and the modified Mann-Kendall trend test were applied to analyze the nonlinear components and trends of the climatic variable and drought indices. Four and six climatic scenarios were generated in sc-PDSI, I , I , and AI with different combinations of the observed and detrended climatic variables, respectively. In Xinjiang, generally increasing trends in minimal, average, and maximal air temperature ( T , T , T ) and precipitation ( P) were found, whereas a decreasing trend in wind speed at 2 m height ( U ) was observed. There were significantly increasing trends in all of the four studied drought indices. Drought relief was more obvious in northern Xinjiang than in southern Xinjiang. The strong influences of increased P on drought relief and the weak influences of increased T , T , and T on drought aggravation were shown by comparing four drought indices under different climate scenarios. Decreased U had a weak influence on drought, as shown by the AI in different climate scenarios. The weak influences of T and U were considered to be masked by the strong influences of P on droughts. Droughts were expected to be more severe if P did not increase, but were likely milder without an increase in air temperature and with a decrease in U . [ABSTRACT FROM AUTHOR]

This study examines the sampling error uncertainties in the monthly surface air temperature (SAT) change in China over recent decades, focusing on the uncertainties of gridded data, national averages, and linear trends. Results indicate that large sampling error variances appear at the station-sparse area of northern and western China with the maximum value exceeding 2.0 K while small sampling error variances are found at the station-dense area of southern and eastern China with most grid values being less than 0.05 K. In general, the negative temperature existed in each month prior to the 1980s, and a warming in temperature began thereafter, which accelerated in the early and mid-1990s. The increasing trend in the SAT series was observed for each month of the year with the largest temperature increase and highest uncertainty of 0.51 ± 0.29 K (10 year) occurring in February and the weakest trend and smallest uncertainty of 0.13 ± 0.07 K (10 year) in August. The sampling error uncertainties in the national average annual mean SAT series are not sufficiently large to alter the conclusion of the persistent warming in China. In addition, the sampling error uncertainties in the SAT series show a clear variation compared with other uncertainty estimation methods, which is a plausible reason for the inconsistent variations between our estimate and other studies during this period. [ABSTRACT FROM AUTHOR]

Both inter- and intra-annual changes in air temperature are impacted by global climate change. Sine curves, such as T = Asin(ω t + φ), adjust the 10-day mean air temperature T contributed by the corresponding number of 10-day t within a year. Based on the curve, relative indices were defined as follows: A, indicating the annual maximum 10-day air temperature; t and t, representing the starting and ending time of the warm season; t and t, indicating the starting and ending time of the growing season; l and l, denoting the length of the warm and growing seasons. The objective of this study was to evaluate the inter- and intra-annual distribution characteristics of the 10-day mean air temperature and the associated spatial patterns, changing trends, and abrupt points in 128 meteorological stations across Northwestern China during 1961 to 2010 by using the selected indices. The results showed that the 2010s had the fastest increase in air temperature during the study period across the study area. Altitude was an important factor affecting the selected indices. Annual mean air temperature ( T) and A were high in low-altitude areas, where the warm and growing season came earlier and ended later, and vice versa. The values of T and A had increased by 1.65 and 0.65 °C during the past 50 years, respectively. The warm and growing season had come earlier by 9 and 7.5 days and ended later by 7.5 and 6 days during the past 50 years, respectively. Therefore, the warm season has gotten longer by 16 days and the growing season by 14 days in the study area during the past 50 years. The abrupt increase of T, A, t, t, t, t, l, and l detected at the 0.05 significance level took place in the late 1960s and that of A occurred in the late 1980s. The inter- and intra-annual distribution of air temperature in Northwestern China has changed during the last five decades, and it is reasonable to expect those changes to continue for a period under global climate change. [ABSTRACT FROM AUTHOR]

dataset of air temperature and precipitation time series (1959-2008) from 61 stations across Shanxi, China is used to analyze the climate change. The monotonic trends and step (abrupt) trends for annual and seasonal series data of mean air temperature and total precipitation are tested by using Mann-Kendall test and Mann-Whitney test, respectively. The results show that annual mean air temperature has increased by 1.20°C during the past 50 years. Winter, spring, and autumn have experienced a significant increase in air temperature. The step trend for annual mean air temperature is different from, but closely related with, those for seasonal mean air temperature. Spatially, there is an enhanced warming trend from south to north in Shanxi, and the most remarkable warming occurs in northern Shanxi. Annual precipitation has decreased by 99.20 mm during the past half century. The decrease is mainly caused by precipitation decline in rainy season (June-September), though precipitation in post-rainy season (October-November) also tended to decrease. An abrupt decrease in precipitation has occurred since late 1970s. Decrease in precipitation is highest in central Shanxi and in the area along the west fringe between Sanchuan River and Fenhe River in western Shanxi. [ABSTRACT FROM AUTHOR]

Understanding the role of aerosols in global and regional climate change requires the long-term measurements of aerosol optical properties. We use an indirect method to infer aerosol optical depths (AODs) based on atmospheric visibility and water vapor pressure measured at 504 key climate stations in eastern China (east of 100° E) over 1951–2002. Inferred AODs are compared with the MODIS satellite measurements for year of 2002. Results show that AODs averaged over 1951–2000 exhibit large values in Sichuan Basin and Changjiang River Delta, and there are two belts of high AODs, one from Beijing to South China by the middle reaches of Changjiang River and the other from Beijing to Changjiang Delta. Inferred AODs in eastern China show the lowest value in 1960s, increase dramatically in 1980s, and reach maximum in 1990s. The ratios of the regional and decadal mean AOD in 1950s, 1960s, 1970s, 1980s, and 1990s to that in 1960s are 1.085, 1.0, 1.066, 1.195, and 1.22, respectively. Statistical analysis shows that variations in AODs correlate with the changes in precipitation and air temperature in eastern China over the past 50 years. Correlation coefficients between annual mean AOD and precipitation are 0.39, 0.37, and 0.57 in the upper (Sichuan Basin), middle, and lower reaches of the Changjiang River, respectively. In the Sichuan Basin, the increase in annual mean AOD correlates with the reduction in air temperature with a correlation coefficient of −0.33 at 95% confidence level. [ABSTRACT FROM AUTHOR]