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This paper presents a method to homogenize China's surface solar radiation (SSR) data and uses the resulting homogenized SSR data to assess the SSR trend over the period 1958-2016. Neighboring surface sunshine duration (SSD) data are used as reference data to assess the SSR data homogeneity. A principal component analysis is applied to build a reference series, which is proven to be less sensitive to occasional data issues than using the arithmetic mean of data from adjacent stations. A relative or absolute test is applied to detect changepoints, depending on whether or not a suitable reference series is available. A quantile-matching method is used to adjust the data to diminish the inhomogeneities. As a result, 60 out of the 119 SSR stations were found to have inhomogeneity issues. These were mainly caused by changes in instrument and observation schedule. The nonclimatic changes exaggerated the SSR change rates in 1991-93 and resulted in a sudden rise in the national average SSR series, causing an unrealistically drastic trend reversal in the 1990s. This was diminished by the data homogenization. The homogenized data show that the national average SSR has been declining significantly over the period 1958-90; this dimming trend mostly diminished over the period 1991-2005 and was replaced by a brightening trend in the recent decade. From the homogenized SSR data, the 1958-90 and 1958-2005 dimming rate is estimated to be -6.13 ± 0.47 and -5.08 ± 0.27W m-2 decade-1, respectively, and the 2005-16 brightening rate is 6.13 ± 1.77W m-2 decade-1. [ABSTRACT FROM AUTHOR]

In China, summer precipitation contributes a major part of the total precipitation amount in a year and has major impacts on society and human life. Whether any changes in summer precipitation are affected by external forcing on the climate system is an important issue. In this study, an optimal fingerprinting method was used to compare the observed changes of total, heavy, moderate, and light precipitation in summer derived from newly homogenized observation data with the simulations from multiple climate models participating in phase 5 of the Coupled Model Intercomparison Project (CMIP5). The results demonstrate that the anthropogenic forcing signal can be detected and separated from the natural forcing signal in the observed increase of seasonal accumulated precipitation amount for heavy precipitation in summer in China and eastern China (EC). The simulated changes in heavy precipitation are generally consistent with observed change in China but are underestimated in EC. When the changes in precipitation of different intensities are considered simultaneously, the human influence on simultaneous changes in moderate and light precipitation can be detected in China and EC in summer. Changes attributable to anthropogenic forcing explain most of the observed regional changes for all categories of summer precipitation, and natural forcing contributes little. In the future, with increasing anthropogenic influence, the attribution-constrained projection suggests that heavy precipitation in summer will increase more than that from the model raw outputs. Society may therefore face a higher risk of heavy precipitation in the future. [ABSTRACT FROM AUTHOR]

Malaria and dengue fever are among the most important vectorborne diseases in the tropics and subtropics. Average weekly meteorological parameters—specifically, minimum temperature, maximum temperature, humidity, and rainfall—were collected using data from 100 automated weather stations from the Indian Space Research Organization. We obtained district-level weekly reported malaria cases from the Integrated Disease Surveillance Program (IDSP), Department of Health and Family Welfare, Andhra Pradesh, India, for three years, 2014–16. We used a generalized linear model with Poisson distribution and default logarithm-link to estimate model parameters, and we used a quasi-Poisson method with a generalized additive model that uses nonparametric regression with smoothing splines. It appears that higher minimum temperatures (e.g., >24°C) tend to lead to higher malaria counts but lower values do not seem to have an impact on the malaria counts. On the other hand, higher values of maximum temperature (e.g., >32°C) seem to negatively affect the malaria counts. The relationships with rainfall and humidity appear to be not as strong once we account for smooth (weekly) trends and temperatures; both smooth curves seem to hover around zero across all of their values. We note that a rainfall amount between 40 and 50 mm seems to have a positive impact on malaria counts. Our analyses show that the incremental increase in meteorological parameters does not lead to an increase in reported malaria cases in the same manner for all of the districts within the same state. This suggests that other factors such as vegetation, elevation, and water index in the environment also influence disease occurrence. [ABSTRACT FROM AUTHOR]