Abstract

Global surface temperature observational datasets are the basis of global warming studies. In the context of increasing global warming and frequent extreme events, it is essential to improve the coverage and reduce the uncertainty of global surface temperature datasets. The China global Merged Surface Temperature Interim version (CMST-Interim) is updated to CMST 2.0 in this study. The previous CMST datasets were created by merging the China global Land Surface Air Temperature (C-LSAT) with sea surface temperature (SST) data from the Extended Reconstructed Sea Surface Temperature version 5 (ERSSTv5). The CMST2.0 contains three variants: CMST2.0-Nrec (without reconstruction), CMST2.0-Imax, and CMST2.0-Imin (According to their reconstruction area of the air temperature over the sea ice surface in the Arctic region). The reconstructed datasets significantly improve data coverage, whereas CMST2.0-Imax and CMST2.0-Imin have improved coverage in the Northern Hemisphere, up to more than 95 %, and thus increased the long-term trends at global, hemispheric, and regional scales from 1850 to 2020. Compared to CMST-Interim, CMST2.0-Imax and CMST2.0-Imin show a high spatial coverage extended to the high latitudes and are more consistent with a reference of multi-dataset averages in the polar regions. The CMST2.0 datasets presented here are publicly available at the website of figshare, https://doi.org/10.6084/m9.figshare.16929427.v4 (Sun and Li, 2021a) and the CLSAT2.0 datasets can be downloaded at https://doi.org/10.6084/m9.figshare.16968334.v4 (Sun and Li, 2021b).

Highlights

  • Global Surface Temperature (GST) is a key meteorological factor in characterizing climate change and has been widely used for climate change detection and assessment (IPCC, 2013; 2021).GST consists of global Land Surface Air Temperature (LSAT), which is the 2-m air temperature observed by land weather stations, and Sea Surface Temperature (SST) observed by ships, buoys and Argos

  • The original reconstructed version of CMST is the Chinese global merged surface temperature reconstruction dataset CMST-Interim, which is a merge of the reconstructed C-LSAT2.0 and Extended ReconstructedSea Surface Temperature version 5 (ERSSTv5), where the reconstructed C-LSAT2.0 is an ensemble reconstruction dataset upgraded from C-LSAT2.0 (Li et al, 2021) with 756 ensemble

  • This paper describes the composition and construction process of the latest versions of the CLSAT 2.0 and CMST 2.0 ensemble datasets

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Summary

Introduction

Global Surface Temperature (GST) is a key meteorological factor in characterizing climate change and has been widely used for climate change detection and assessment (IPCC, 2013; 2021). Sea Surface Temperature version 5) (Huang et al, 2017) as the ocean component It is generally consistent with other global datasets in terms of GST trends and uncertainty levels since 1880 (Li et al, 2020). With different parameter schemes, combined with the observation constraint method, to fill the data default region of C-LSAT2.0 and released the new reconstructed dataset C-LSAT2.0 ensemble and the global surface temperature dataset CMST-Interim. In the current CMST-Interim (Sun et al, 2021) and its earlier version (Yun et al, 2019), we still fully adopted the setting from ERSSTv5, which treats the sea ice region in the Arctic as the sea surface temperature below the sea ice and assigns a default value (1.8°C), which makes it still a gap in the polar region.

Updates of the land and ocean datasets
Sea surface temperature
Sea ice surface air temperature
CMST and its brief reconstruction history
Reconstruction of the terrestrial component
Reconstruction of the ocean component
Reconstruction of Arctic ice surface temperature
Total uncertainty of LSAT
Uncertainty of global surface temperature
Summary and Prospects
Full Text
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