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

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 https://doi.org/10.5281/zenodo.5746632 (Wei et al., 2021). [ABSTRACT FROM AUTHOR]