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

Abstract

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

Assessing spatiotemporal variation of heat waves during 1961–2016 across mainland China

“…We found that over half of the stations showed an increasing trend for EHFs, and change points mainly occurred during 1990s, especially in 1994, a common phenomenon for HWs across the world that was confirmed by many other scholars (Ding et al ., ; Fischer and Schär, ; Wang et al ., ). Meanwhile, we found a dramatic increase of EHFs since the 1990s on a countrywide scale; this phenomenon is consistent with the previous conclusion of the surface air temperature change pattern (Ren et al ., ; Xu et al ., ).…”

Evaluating the performance of WRF urban schemes and PBL schemes over Dallas Fort Worth during a dry summer and a wet summer

“…Several homogenized in situ datasets have been provided for analysis of the interpolation of the surface air temperature data during recent years (Li et al 2010(Li et al , 2017W. Xu et al 2013;Cao et al 2013Cao et al , 2017.…”

“…Here, a temperature station dataset of monthly averages from 1900 to 1990 was used for spatial interpolation. This dataset was constructed by the National Meteorological Information Centre of China, and the temperature records in the dataset have has been quality controlled and homogenized (Li et al 2010(Li et al , 2017W. Xu et al 2013).…”

MOTEDAS century: A new high‐resolution secular monthly maximum and minimum temperature grid for the Spanish mainland (1916–2015)

“…Thus temperature, precipitation, humidity, sunshine, pressure, etcetera, data from ground weather stations continue to be the best source of information for analysing the evolution of the climate over the last 150 years, place the current period in its temporal context, and create databases to evaluate results from the models. This type of data have been compiled by several research centres and resulted in databases covering the world (see the review by Strangeways, 2010), and are well‐known by their acronyms: GHCN (Lawrimore et al ., 2011), GISS (Hansen et al ., 2010), HadCRUTEM4 (Jones et al ., 2012), BEST (Rohde et al ., 2013), which, with some recent contributions (Xu et al ., 2018) are complemented by others created at continental, national and regional scales.…”