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photo of Zheng Duan on Lund webpage

Zheng Duan

Associate senior lecturer

photo of Zheng Duan on Lund webpage

Deep-learning-based harmonization and super-resolution of near-surface air temperature from CMIP6 models (1850–2100)

Author

  • Xikun Wei
  • Guojie Wang
  • Donghan Feng
  • Zheng Duan
  • Daniel Fiifi Tawia Hagan
  • Liangliang Tao
  • Lijuan Miao
  • Buda Su
  • Tong Jiang

Summary, in English

Future global temperature change will have significant effects on society and ecosystems. Earth system models (ESM) are the primary tools to explore future climate change. However, ESMs have great uncertainty and often run at a coarse spatial resolution (usually about 2°). Accurate high-spatial-resolution temperature dataset are needed to improve our understanding of temperature variations and for many other applications. We apply Super resolution (SR) in computer vision using deep learning (DL) to merge 31 ESMs data. The proposed method performs data merging, bias-correction, and spatial downscaling simultaneously. The CRU TS (Climate Research Unit gridded Time Series) data is used as reference data in the model training process. To find a suitable DL method, we select five SR methodologies with different structures. We compare the performances of the methods based on mean square error (MSE), mean absolute error (MAE) and Pearson correlation coefficient (R). The best method is used to merge the projected monthly data (1850–1900), and monthly future scenarios data (2015–2100), at the high spatial resolution of 0.5°. Results show that the merged data have considerably improved performance compared with individual ESM data and their ensemble mean (EM), both spatially and temporally. The MAE shows significant improvement; the spatial distribution of the MAE widens along the latitudes in the Northern Hemisphere. The MAE of merged data is ranging from 0.60 to 1.50, the South American (SA) has the lowest error and the Europe has the highest error. The merged product has excellent performance when the observation data is smooth with few fluctuations in the time series. This work demonstrates the applicability and effectiveness of the DL methods in data merging, bias-correction and spatial downscaling when enough training data are available. Data can be accessed at https://doi.org/10.5281/zenodo.5746632.

Department/s

  • Dept of Physical Geography and Ecosystem Science
  • BECC: Biodiversity and Ecosystem services in a Changing Climate
  • MERGE: ModElling the Regional and Global Earth system

Publishing year

2023

Language

English

Pages

1461-1479

Publication/Series

International Journal of Climatology

Volume

43

Issue

3

Document type

Journal article

Publisher

John Wiley & Sons Inc.

Topic

  • Earth and Related Environmental Sciences
  • Computer and Information Science

Keywords

  • climate change
  • CMIP6
  • data-merging
  • deep-learning
  • temperature

Status

Published

ISBN/ISSN/Other

  • ISSN: 0899-8418