Preprints
https://doi.org/10.5194/essd-2022-83
https://doi.org/10.5194/essd-2022-83
 
15 Mar 2022
15 Mar 2022
Status: a revised version of this preprint was accepted for the journal ESSD and is expected to appear here in due course.

A global dataset of spatiotemporally seamless daily mean land surface temperatures: generation, validation, and analysis

Falu Hong1, Wenfeng Zhan1,2, Frank-M. Göttsche3, Zihan Liu1, Pan Dong1, Huyan Fu1, Fan Huang1, and Xiaodong Zhang4 Falu Hong et al.
  • 1Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology, International Institute for Earth System Science, Nanjing University, Nanjing, Jiangsu 210023, China
  • 2Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing 210023, China
  • 3Karlsruhe Institute of Technology (KIT), Hermann-von-Helmholtz-Platz 1, 76344 Eggenstein-Leopoldshafen, Germany
  • 4Shanghai Spaceflight Institute of TT&C and Telecommunication, Shanghai, 201109, China

Abstract. Daily mean land surface temperatures (LSTs) acquired from polar-orbiters are crucial for various applications such as global and regional climate change analysis. However, thermal sensors from polar-orbiters can only sample the surface effectively with very limited times per day under cloud-free conditions. These limitations have produced a systematic sampling bias (ΔTsb) on the daily mean LST (Tdm) estimated with the traditional method, which uses the averages of clear-sky LST observations directly as the Tdm. Several methods have been proposed for the estimation of the Tdm, yet they become less capable of generating spatiotemporally seamless Tdm across the globe. Based on MODIS and reanalysis data, here we proposed an improved annual and diurnal temperature cycle-based framework (termed the IADTC framework) to generate global spatiotemporally seamless Tdm products ranging from 2003 to 2019 (named as the GADTC products). The validations show that the IADTC framework reduces the systematic ΔTsb significantly. When validated only with in situ data, the assessments show that the mean absolute errors (MAEs) of the IADTC framework are 1.4 K and 1.1 K for SURFRAD and FLUXNET data, respectively; and the mean biases are both close to zero. Direct comparisons between the GADTC products and in situ measurements indicate that the MAEs are 2.2 K and 3.1 K for the SURFRAD and FLUXNET datasets, respectively; and the mean biases are −1.6 K and −1.5 K for these two datasets, respectively. By taking the GADTC products as references, further analysis reveals that the Tdm estimated with the traditional averaging method yields a positive systematic ΔTsb of greater than 2.0 K in low- and mid-latitude regions while of a relatively small value in high-latitude regions. Although the global mean LST trend (2003 to 2019) calculated with the traditional method and the IADTC framework is relatively close (both between 0.025 to 0.029 K/year), regional discrepancies in LST trend does occur – the pixel-based MAE in LST trend between these two methods reaches 0.012 K/year. We consider the IADTC framework can guide the further optimization of Tdm estimation across the globe; and the generated GADTC products should be valuable in various applications such as global and regional warming analysis. The GADTC products are freely available at https://doi.org/10.5281/zenodo.6287052 (Hong et al., 2022).

Falu Hong et al.

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on essd-2022-83', Anonymous Referee #1, 05 Apr 2022
    • AC1: 'Reply on RC1', W. Zhan, 15 Jun 2022
  • RC2: 'Comment on essd-2022-83', Anonymous Referee #2, 18 Apr 2022
    • AC2: 'Reply on RC2', W. Zhan, 15 Jun 2022
  • RC3: 'Comment on essd-2022-83', Anonymous Referee #3, 18 Apr 2022
    • AC3: 'Reply on RC3', W. Zhan, 15 Jun 2022

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on essd-2022-83', Anonymous Referee #1, 05 Apr 2022
    • AC1: 'Reply on RC1', W. Zhan, 15 Jun 2022
  • RC2: 'Comment on essd-2022-83', Anonymous Referee #2, 18 Apr 2022
    • AC2: 'Reply on RC2', W. Zhan, 15 Jun 2022
  • RC3: 'Comment on essd-2022-83', Anonymous Referee #3, 18 Apr 2022
    • AC3: 'Reply on RC3', W. Zhan, 15 Jun 2022

Falu Hong et al.

Data sets

A global spatiotemporally seamless daily mean land surface temperature from 2003 to 2019 Falu Hong, Wenfeng Zhan, Frank-M. Göttsche, Zihan Liu, Pan Dong, Huyan Fu, Fan Huang, Xiaodong Zhang https://doi.org/10.5281/zenodo.6287052

Falu Hong et al.

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Short summary
Daily mean land surface temperature (LST) acquired from satellite thermal sensors is crucial for various applications such as global and regional climate change analysis. This study proposed a framework to generate global spatiotemporally seamless daily mean LST products (2003–2019). Validations show that the products outperform the traditional method with satisfying accuracy. Our further analysis reveals that the LST-based global land surface warming rate is 0.029 K/year from 2003 to 2019.