
Zheng Duan
Senior lecturer

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