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.
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This study was supported by the National Special Public Welfare Research Fund (Nos. GYHY201406016 and GYHY201206012), China Meteorological Administration Special Foundation for Climate Change (CCSF201438), and the Natural Science Foundation of China (Nos. 91546117 and 71373131). We thank the many contributors of data, which made establishment of the dataset possible.
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All the datasets have been made available through the national meteorological services website (http://10.1.64.154/portal/web-home.index).
Monthly updates will be based on a three-step procedure (Fig. 13), as follows. (1) The first round updates are based on hourly real-time data from the WMO Global Telecommunications System (GTS) and Integrated Surface Database (ISD) from NCEI. The daily values are calculated every day from the merged (GTS and ISD) and quality-controlled hourly datasets, and then the monthly temperature is obtained and updated in the dataset at the beginning of the following month. (2) Monthly mean values from CLIMAT message through GTS are provided to update the dataset on about the 20th day of the following month; this is the second round of dataset update. In the two steps, only the WMO stations are matched to be updated. The identification of the same station is performed by the WMO station number. (3) Other monthly data from existing homogenized datasets of the data sources listed in Table 1 are accessed and updated to the dataset. This round of updates is not carried out regularly and must be based on the update of each homogenization data source. Generally speaking, the last round of data update replaces the previous two rounds if the stations are found to be the same in previous rounds of data update. In the step, the identification of the same station is performed by the data source symbol and the station number. It is worth noting that, there is no new merging algorithm to be used during the process of updating.
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Xu, W., Li, Q., Jones, P. et al. A new integrated and homogenized global monthly land surface air temperature dataset for the period since 1900. Clim Dyn 50, 2513–2536 (2018). https://doi.org/10.1007/s00382-017-3755-1
- CMA-LSAT dataset
- Surface air temperature
- Climate change