[1] This study first homogenizes time series of daily maximum and minimum temperatures recorded at 825 stations in China over the period from 1951 to 2010, using both metadata and the penalized maximum t test with the first-order autocorrelation being accounted for to detect change points and using the quantile-matching algorithm to adjust the data time series to diminish discontinuities. Station relocation was found to be the main cause for discontinuities, followed by station automation. The effects of discontinuities on estimation of long-term trends in the annual mean and extreme indices of temperature are illustrated. The data homogenization is shown to have improved the spatial consistency of estimated trends. Using the homogenized daily minimum and daily maximum temperature data, this study also analyzes trends in extreme temperature indices. The results show that the vast majority (85%-90%) of the 825 sites have experienced significantly more warm nights and less cold nights since 1951. There have also been more warm days and less cold days since 1951, although these trends are less extensive. About 62% of the 825 sites were found to have experienced significantly more warm days and about 50% significantly less cold days. None of the 825 sites were found to have significantly more cold nights/days or less warm nights/days. These indicate that the warming is stronger in nighttime than in daytime and stronger in winter than in summer. Thus, the diurnal temperature range was found to have significantly decreased at 49% of the 825 sites, with significant increases being identified only at 3% of these sites.

ABSTRACT:A new homogenized temperature data set called the China Homogenized Historical Temperature Dataset (CHHTD-V1.0) has been developed, and it includes daily and monthly mean temperature series from 2419 national stations distributed throughout mainland China for the period from 1951 to the present. The inhomogeneities in individual station series were detected using a penalized maximum t-test (PMT) that accounted for the first-order autocorrelation. Detailed metadata information was applied to validate the changepoints caused by changes in local observation systems. Comparative analyses suggested that the quantile-matching (QM) adjustments that accounted for high-order discontinuities led to more reasonable results than the MEAN adjustments for the daily temperature series. Therefore, the QM method was applied to adjust the discontinuities caused by non-climate changes such as changing of observing site, instrumentation and observation environments. The physical consistency among the daily maximum, mean and minimum temperatures (T max , T m and T min ) was also checked for each station. Based on the new homogenized data set, linear trends in the annual and seasonal temperature series from 1960 to 2014 were calculated. In comparison with the original data set, the homogenized data set improves the geographical consistency of the long-term climate trends over the region. The updated nationwide mean warming rate reached 0.22 ∘ C per 10 years for the T max , 0.27 ∘ C per 10 years for the T m and 0.38 ∘ C per 10 years for the T min from 1960 to 2014, which are considerably larger than the previous estimates that were based on the more frequently used networks of a few hundred stations in China.

[1] Based on the PRECIS climate model system, we simulate the distribution of the present (1961$1990) and future (2071$2100) extreme climate events in China under the IPCC SRES B2 scenario. The results show that for the present case PRECIS simulates well the spatial distribution of extreme climate events when compared with observations. In the future the occurrence of hot events is projected to be more frequent and the growing season will lengthen, while the occurrence of cold events is likely to be much rarer. A warming environment will also give rise to changes in extreme precipitation events. There would be an overall increasing trend in extreme precipitation events over most of China. The southeast coastal zone, the middle and lower reaches of the Yangtze River and North China are projected to experience more extreme precipitation than the present.

[1] In this study, we bring together different source data sets and use quality control, interpolation, and homogeneity methods to construct a set of homogenized monthly mean surface air temperature (SAT) series for 18 stations in eastern and central China from the late nineteenth century. Missing values are statistically interpolated, and cross validation is used to assess the accuracy of the interpolation approaches. Results show that the errors of interpolation are small, and the interpolated values are statistically acceptable. Multiple homogeneity methods and all available metadata are used to assess the consistency of the time series and then to develop adjustments when necessary. Thirty-three homogeneity breakpoints are detected in the 18 stations, and the time series is adjusted to the latest segment of the data series. The adjusted annual mean SAT generally shows a range of trends of 1.0°to 4.2°C/100 years in northeastern and southeastern China and a range of trends of À0.3°to 1.0°C/100 years in central China near 30°N. Compared to the adjusted time series, the unadjusted time series underestimates the warming trend during the past 100 years. The regional and annual mean SAT over eastern and central China agrees well with estimates from a much denser station network over this region of China since 1951 and shows a warming trend of 1.52°C/100 years during 1909-2010.

Based on homogenized land surface air temperature (SAT) data (derived from China Homogenized Historical Temperature (CHHT) 1.0), the warming trends over Northeast China are detected in this paper, and the impacts of urban heat islands (UHIs) evaluated. Results show that this region is undergoing rapid warming: the trends of annual mean minimum temperature (MMIT), mean temperature (MT), and mean maximum temperature (MMAT) are 0.40 C decade-1, 0.32 C decade-1, and 0.23 C decade-1, respectively. Regional average temperature series built with these networks including and excluding "typical urban stations" are compared for the periods of 1954-2005. Although impacts of UHIs on the absolute annual and seasonal temperature are identified, UHI contributions to the long-term trends are less than 10% of the regional total warming during the period. The large warming trend during the period is due to a regime shift in around 1988, which accounted for about 51% of the regional warming

Using the reconstructed continuous and homogenized surface air temperature (SAT) series for 16 cities across eastern China (where the greatest industrial developments in China have taken place) back to the nineteenth century, the authors examine linear trends of SAT. The regional-mean SAT over eastern China shows a warming trend of 1.52°C (100 yr)−1 during 1909–2010. It mainly occurred in the past 4 decades and this agrees well with the variability in another SAT series developed from a much denser station network (over 400 sites) across this part of China since 1951. This study collects population data for 245 sites (from these 400+ locations) and split these into five equally sized groups based on population size. Comparison of these five groups across different durations from 30 to 60 yr in length indicates that differences in population only account for between 9% and 24% of the warming since 1951. To show that a larger urbanization impact is very unlikely, the study additionally determines how much can be explained by some large-scale climate indices. Anomalies of large-scale climate indices such as the tropical Indian Ocean SST and the Siberian atmospheric circulation systems account for at least 80% of the total warming trends.

PurposeThe outbreak of the novel COVID-19 virus has spread throughout the world, causing unprecedented disruption to not only China's agricultural trade but also the world's agricultural trade at large. This paper attempts to provide a preliminary analysis of the impact of the COVID-19 pandemic on China's agricultural importing and exporting from both short- and long-term perspectives.Design/methodology/approachThis study seeks to analyze how the outbreak of COVID-19 could potentially impact China's agricultural trade. With respect to exports, the authors have pinpointed major disruptive factors arising from the pandemic which have affected China's agricultural exports in both the short and long term; in doing so, we employ scenario analysis which simulates potential long-term effects. With regard to imports, possible impacts of the pandemic regarding the prospects of food availability in the world market are investigated. Using scenario analysis, the authors estimate the potential change in China's food market—especially meat import growth—in light of the implementation of the newly signed Sino-US Economic and Trade Agreement (SUETA).FindingsThe results show that China's agricultural exports have been negatively impacted in the short-term, mostly due to the disruption of the supply chain. In the long term, dampened external demand and potential imposition of non-tariff trade barriers (NTBs) will exert more profound and lasting negative effects on China's agricultural export trade. On the other hand, despite panic buying and embargoing policies from some exporting and importing countries, the world food availability and China's food import demand are still optimistic. The simulation results indicate that China's import of pork products, in light of COVID-19 and the implementation of SUETA, would most likely see a sizable climb in quantity, but a lesser climb in terms of value.Originality/valueAgricultural trade in China has been a focal-point of attention in recent years, with new challenges slowing exports and increasing dependence on imports for food security. The outbreak of the COVID-19 pandemic adds significant uncertainty to agricultural trade, giving rise to serious concerns regarding its potential impact. By exploring the impact of the unprecedented pandemic on China's agricultural trade, this study should contribute to a better understanding of the still-evolving pandemic and shed light on pertinent policy implications.

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