A new integrated and homogenized global monthly land surface air temperature dataset for the period since 1900

A new dataset of integrated and homogenized monthly surface air temperature over global land for the period since 1900 [China Meteorological Administration global Land Surface Air Temperature (CMA-LSAT)] is developed. In total, 14 sources have been collected and integrated into the newly developed dataset, including three global (CRUTEM4, GHCN, and BEST), three regional and eight national sources. Duplicate stations are identified, and those with the higher priority are chosen or spliced. Then, a consistency test and a climate outlier test are conducted to ensure that each station series is quality controlled. Next, two steps are adopted to assure the homogeneity of the station series: (1) homogenized station series in existing national datasets (by National Meteorological Services) are directly integrated into the dataset without any changes (50% of all stations), and (2) the inhomogeneities are detected and adjusted for in the remaining data series using a penalized maximal t test (50% of all stations). Based on the dataset, we re-assess the temperature changes in global and regional areas compared with GHCN-V3 and CRUTEM4, as well as the temperature changes during the three periods of 1900–2014, 1979–2014 and 1998–2014. The best estimates of warming trends and there 95% confidence ranges for 1900–2014 are approximately 0.102 ± 0.006 °C/decade for the whole year, and 0.104 ± 0.009, 0.112 ± 0.007, 0.090 ± 0.006, and 0.092 ± 0.007 °C/decade for the DJF (December, January, February), MAM, JJA, and SON seasons, respectively. MAM saw the most significant warming trend in both 1900–2014 and 1979–2014. For an even shorter and more recent period (1998–2014), MAM, JJA and SON show similar warming trends, while DJF shows opposite trends. The results show that the ability of CMA-LAST for describing the global temperature changes is similar with other existing products, while there are some differences when describing regional temperature changes

Monitoring and understanding trends in extreme storms: State of knowledge

Review of the climate science for severe convective storms, extreme precipitation, hurricanes and typhoons, and severe snowstorms and ice storms in the US shows that the ability to detect and attribute trends varies, depending on the phenomenon. A specific subset of extreme weather and climate types affecting the country is discussed to examine these extreme weather conditions. The categories of storms described were selected as they caused property damage and loss of life. The identification of an extreme occurrence was based on meteorological properties in place of the destructiveness. The primary purpose was to examine the scientific evidence for the prevailing capability to detect trends and understand their causes for certain weather types, including severe convective storms and hurricanes and typhoons