10 Dec 2021

10 Dec 2021

Review status: this preprint is currently under review for the journal ESSD.

Deep-Learning-Based Harmonization and Super-Resolution of Near-Surface Air Temperature from CMIP6 Models (1850–2100)

Xikun Wei1, Guojie Wang1, Donghan Feng1, Zheng Duan2, Daniel Fiifi Tawia Hagan1, Liangliang Tao1, Lijuan Miao1, Buda Su1, and Tong Jiang1 Xikun Wei et al.
  • 1School of Geographical Science, Nanjing University of Information Science and Technology (NUIST), Nanjing 210044, China
  • 2Department of Physical Geography and Ecosystem Science, Lund University, 223 62 Lund, Sweden

Abstract. Future global temperature change would have significant effects on society and ecosystems. Earth system models (ESM) are the primary tools to explore the future climate change. However, ESMs still exist great uncertainty and often run at a coarse spatial resolution (The majority of ESMs at about 2 degree). Accurate temperature data at high spatial resolution are needed to improve our understanding of the temperature variation and for many applications. We innovatively apply the deep-learning(DL) method from the Super resolution (SR) in the computer vision to merge 31 ESMs data and the proposed method can perform data merge, bias-correction and spatial-downscaling simultaneously. The SR algorithms are designed to enhance image quality and outperform much better than the traditional methods. The CRU TS (Climate Research Unit gridded Time Series) is considered as reference data in the model training process. In order to find a suitable DL method for our work, we choose five SR methodologies made by different structures. Those models are compared based on multiple evaluation metrics (Mean square error(MSE), mean absolute error(MAE) and Pearson correlation coefficient(R)) and the optimal model is selected and used to merge the monthly historical data during 1850–1900 and monthly future scenarios data (SSP1-2.6, SSP2-4.5, SSP3-7.0, SSP5-8.5) during 2015–2100 at the high spatial resolution of 0.5 degree. Results showed that the merged data have considerably improved performance than any of the individual ESM data and the ensemble mean (EM) of all ESM data in terms of both spatial and temporal aspects. The MAE displays a great improvement and the spatial distribution of the MAE become larger and larger along the latitudes in north hemisphere, presenting like a ‘tertiary class echelon’ condition. The merged product also presents excellent performance when the observation data is smooth with few fluctuations in time series. Additionally, this work proves that the DL model can be transferred to deal with the data merge, bias-correction and spatial-downscaling successfully when enough training data are available. Data can be accessed at (Wei et al., 2021).

Xikun Wei et al.

Status: open (until 19 Feb 2022)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse

Xikun Wei et al.

Data sets

Deep-Learning-Based Harmonization and Super-Resolution of Near-Surface Air Temperature from CMIP6 Models (1850-2100) Xikun Wei, Guojie Wang, Donghan Feng, Zheng Duan,Daniel Fiifi Tawia Hagan , Liangliang Tao, Lijuan Miao, Buda Su, Jiang Tong

Xikun Wei et al.


Total article views: 358 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
280 74 4 358 20 2 7
  • HTML: 280
  • PDF: 74
  • XML: 4
  • Total: 358
  • Supplement: 20
  • BibTeX: 2
  • EndNote: 7
Views and downloads (calculated since 10 Dec 2021)
Cumulative views and downloads (calculated since 10 Dec 2021)

Viewed (geographical distribution)

Total article views: 347 (including HTML, PDF, and XML) Thereof 347 with geography defined and 0 with unknown origin.
Country # Views %
  • 1
Latest update: 28 Jan 2022
Short summary
In this study, we use the deep learning (DL) method to generate the temperature data for the global land (except Antartica) at higher spatial resolution (0.5 degree) based on 31 different CMIP6 Earth system model(ESM). Our methods can perform bias correction, spatial downscaling and data merging simultaneously. The merged data have a remarkably better quality compared with the individual ESMs in terms of both spatial dimension and time dimension.