The Global Surface Temperature (ST) dataset is the foundation of global climate change research. Different teams from the National Oceanic and Atmospheric Administration (NCEI), National Aeronautics and Space Administration (GISS), Met Office Hadley Centre and UEA CRU, as well as Berkeley Earth, have developed multiple global surface temperature datasets. This study proposes a new global ST dataset called "China Consolidated Surface Temperature (CMST)". CMST is a fusion of China's land surface temperature (C-LSAT1.3) and extended reconstructed sea surface temperature (ERSSTv5) sea surface temperature (SST) data. The merger of C-LSAT and ERSSTv5 shows high spatial coverage extending to high latitudes, and is more consistent with the average reference of multiple datasets in polar regions. Comparison shows that from 1900 to 2017, the interannual, decadal, and long-term trends of CMST are consistent with other existing global ST datasets at the global, hemisphere, and regional scales. The CMST dataset can be used for global climate change assessment, monitoring, and detection.
Surface temperature data: The C-LSAT1.0 dataset processed SAT data since 1900, with a total of 14 data sources, including 3 global data sources (CRUTEM 4.6, GHCNv3, and BEST), 3 regional data sources from the Scientific Committee on Antarctic Research, daily datasets from the European Climate Assessment and Dataset (ECA&D) and the Alpine Historical Instrument Climate Surface Time Series (HISTALP), 2004, daily datasets from the European Climate Assessment and Dataset (ECA&D), the Alpine Historical Instrument Climate Surface Time Series (HISTALP), as well as eight national data sources from China, the United States, Russia, Canada, Australia, South Korea, Japan, and Vietnam.
Sea surface temperature data: SST dataset.
The merger process of C-LSAT1.3 and ERSST:
C-LSAT and ERSSTv5 calculated the outliers related to the 1961-1990 base period in each grid box.
2. For the ocean land boundary, the specific process of the ratio of land and ocean area is as follows:
a. Reduce land (C-LSAT1.3) and ocean data to resolution. The resolution of ocean data is distributed in four grids The resolution of land data is distributed across 25 grids
b. Use the ocean land mask file to distinguish all global grids as either land or sea.
c. The ocean grid data and land grid data are concatenated through ocean land masks to obtain global ST grid data.
d. Calculate the average surface temperature anomaly (STA) of the grid.
The spatial coverage of the merger of C-LSAT1.3 and ERSSTv5 is greater than that of the merger of C-LSAT1.3 and HadSST3, especially in polar regions. In addition, the former (merging C-LSAT1.3 with ERSSTv5 and naming it CMST) is also superior in terms of spatial distribution and temporal variation.
The LSAT in CMST uses high-quality C-LSAT1.3. More than 4900 stations were added to the previous version of C-LSAT1.0 (Xu et al., 2018), which further expanded data coverage. The newly added stations are mainly from the ISTI dataset. The SST in CMST combines ERSSTv5 with ocean data from the latest ICOADS R3.0 and includes multiple types of observations. Compared to other existing global ST datasets, CMST increases overall coverage of land and ocean surfaces worldwide.
The time series of CMST in global and mid low latitude regions are consistent with other merged datasets, including interannual and decadal time scales. In the high latitude regions of NH and SH, the difference in temperature trends is usually significant, and the trend of CMST represents the main long-term climate change. Therefore, the temperature trend of CMST from 1900 to 2017 is generally consistent with other datasets and has been proven to be a new and useful tool for studying global climate change.
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CSTR:11738.11.NCDC.PANGAEA.DB6656.2024
10.1594/PANGAEA.901295